├── +biqaa ├── blindimagequality.m ├── localwigner.m └── renyientropy.m ├── +biqi ├── biqi.m ├── jpeg_quality_score.m ├── model_89 ├── model_89_blur ├── model_89_ff ├── model_89_jp2k ├── model_89_wn ├── range2 ├── range2_blur ├── range2_ff ├── range2_jp2k ├── range2_wn └── readme.txt ├── +bliinds2 ├── bliinds2_feature_extraction.m ├── bliinds2_score.m ├── bliinds_prediction.m ├── blinds_test.m ├── dct_freq_bands.m ├── fitting.m ├── gama_dct.m ├── gama_gen_gauss.m ├── oriented1_dct_rho_config3.m ├── oriented2_dct_rho_config3.m ├── oriented3_dct_rho_config3.m ├── parameters_from_training.mat ├── rho_dct.m └── rho_gen_gauss.m ├── +brisque ├── allmodel ├── allrange ├── brisque_feature.m ├── brisquescore.m ├── estimateaggdparam.m ├── estimateggdparam.m ├── readme.txt ├── svm-predict.exe1 └── svm-scale.exe1 ├── +divine ├── data_live_trained.mat ├── divine.m ├── divine_feature_extract.m ├── divine_overall_quality.m ├── find_spatial_hist_fast.m ├── map_matrix_to_closest_vec.m ├── norm_sender_normalized.m ├── readme.txt └── ssim_index_new.m ├── +iqvg ├── IQVG.m ├── SDSP.m ├── buildHistogram.m ├── fun_gabor_fv.m ├── iqvg_test.m ├── model.mat ├── outSVMData.m ├── readme.txt ├── sample_img.m └── scale_parameter ├── +niqe ├── computefeature.m ├── computemean.m ├── computequality.m ├── estimateaggdparam.m ├── estimatemodelparam.m ├── modelparameters.mat ├── niqe.m └── readme.txt ├── README.md ├── computeQualityMetrics.m └── examples ├── image.jpg └── simpleExample.m /+biqaa/blindimagequality.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dsoellinger/blind_image_quality_toolbox/4d12c43c77bba538f684df0b62621e9350854c43/+biqaa/blindimagequality.m -------------------------------------------------------------------------------- /+biqaa/localwigner.m: -------------------------------------------------------------------------------- 1 | function W=localwigner(X,N,theta,units,period,window_shape) 2 | %% 3 | % W=localwigner(X,N,theta,units,window_shape) 4 | % Pixel-wise Pseudo-Wigner distribution for image X, calculated with a 5 | % N-pixels-1D oriented window 6 | % Inputs: 7 | % X: input grayscale image. Gray-levels must be double precision 8 | % non-negative values in the range 0 to 255 (or normalized to 9 | % the [0, 1] interval) 10 | % N: operational window size (MUST BE AN EVEN NUMBER). The algorithm takes 11 | % an array of N pixels arranged in direction theta. 12 | % theta: a number giving the direction in radians, degrees or slope 13 | % units: according to input theta: 'radian', 'degree' or 'slope' 14 | % period: (optional, when ommited the function is calculated as aperiodic) 15 | % 'periodic'(takes the N+1 pixel value equal to the value in N=1) 16 | % 'aperiodic' (takes the N+1 pixel value equal to the value 17 | % determined by the image in position N+1) 18 | % window_shape: (optional, whe ommited the window is considered as square) 19 | % 'square', 'circular' 20 | % Outputs: 21 | % W: pixelwise Pseudo-Wigner Distribution of image X 22 | % W(i,j,:) is the PWD of pixel X(i,j) 23 | % By Salvador Gabarda 2011 24 | % salvador@optica.csic.es 25 | 26 | %% 27 | X=double(X); 28 | X=X+eps; % avoid zeros 29 | [ro,co]=size(X); 30 | if nargin<6 31 | v=direction(N,theta,units,'square'); 32 | else % when 'circular' shape is determined in the input, values are 33 | %calculated by bilinear interpolation and some smoothing effect 34 | % will be expected 35 | v=direction(N,theta,units,window_shape); 36 | end 37 | 38 | AI=zeros(ro,co,N); 39 | AD=zeros(ro,co,N); 40 | A=zeros(ro,co,N); 41 | 42 | for k=1:N 43 | if k<=N/2+1 44 | iz=v(:,:,k); 45 | [IIx,IIy]=find(iz~=0); 46 | izx=zeros(N+1,1); 47 | izx(IIx,1)=1; 48 | izy=zeros(1,N+1); 49 | izy(1,IIy)=1; 50 | IIIx=(N+2)-IIx; 51 | IIIy=(N+2)-IIy; 52 | dex=zeros(N+1,1); 53 | dex(IIIx,1)=1; 54 | dey=zeros(1,N+1); 55 | dey(1,IIIy)=1; 56 | AI(:,:,k)=conv2(conv2(X,izx,'same'),izy,'same'); 57 | AD(:,:,k)=conv2(conv2(conj(X),dex,'same'),dey,'same'); 58 | A(:,:,k)=AD(:,:,k).*AI(:,:,k); 59 | if nargin>4 60 | switch period 61 | case 'periodic' 62 | % this bound is required when the function is not real 63 | if k==1 64 | A(:,:,k)=AI(:,:,k).*conj(AI(:,:,k)); 65 | end 66 | case 'aperiodic' 67 | end 68 | end 69 | else 70 | A(:,:,k)=A(:,:,N+2-k); 71 | end 72 | end 73 | 74 | Y=fft(A,[],3); 75 | W=fftshift(Y,3); 76 | W=real(W); 77 | 78 | 79 | function H=direction(N,theta,units,window_shape) 80 | %% 81 | % Position correction 82 | switch units 83 | case 'radian' 84 | arad=theta-pi/2; 85 | m=tan(arad); 86 | case 'degree' 87 | theta=theta-90; 88 | arad=deg2rad(theta); 89 | m=tan(arad); 90 | case 'slope' 91 | m=theta; 92 | m=-1/(m+eps); 93 | arad=atan(m); 94 | end 95 | 96 | % Generatriz 97 | if abs(m)<1 98 | x=(-N/2:N/2); 99 | y=round(m*x); 100 | h=zeros(N+1,N+1); 101 | x=x+N/2+1; 102 | y=y+N/2+1; 103 | for k=1:N+1 104 | h(x(k),y(k))=1; 105 | end 106 | elseif abs(m)>1 107 | y=(-N/2:N/2); 108 | x=round(m^-1*y); 109 | h=zeros(N+1,N+1); 110 | x=x+N/2+1; 111 | y=y+N/2+1; 112 | for k=1:N+1 113 | h(x(k),y(k))=1; 114 | end 115 | elseif abs(m)==1 116 | h=diag(ones(1,N+1)); 117 | end 118 | 119 | % Individualisation 120 | H=zeros(N+1,N+1,N+1); 121 | if abs(m)<=1 122 | for k=1:N+1 123 | H(:,:,k)=zeros(N+1); 124 | H(k,:,k)=h(k,:); 125 | end 126 | elseif abs(m)>1 127 | for k=1:N+1 128 | H(:,:,k)=zeros(N+1); 129 | H(:,k,k)=h(:,k); 130 | end 131 | end 132 | 133 | switch window_shape 134 | case 'circular' 135 | 136 | % Interpolation 137 | narad=arad+pi/2; 138 | U=zeros(N+1,N+1,N+1); 139 | 140 | % angle 141 | phi=mod(narad,pi); 142 | 143 | %for k=1:N+1 144 | for k=1:N+1 145 | % interpolation position 146 | x=(N+2)/2-(k-(N+2)/2)*sin(phi); 147 | y=(N+2)/2+(k-(N+2)/2)*cos(phi); 148 | 149 | % boundary positions 150 | x0=fix(x); 151 | x1=fix(x)+1; 152 | y0=fix(y); 153 | y1=fix(y)+1; 154 | 155 | % weight factors 156 | fx=x-fix(x); 157 | fy=y-fix(y); 158 | w00=(1-fx)*(1-fy); 159 | w01=(1-fx)*fy; 160 | w10=fx*(1-fy); 161 | w11=fx*fy; 162 | 163 | % calculation masks 164 | if and(x0<=N+1,y0<=N+1) 165 | U(x0,y0,N+2-k)=w00; 166 | end 167 | if and(x0<=N+1,y1<=N+1) 168 | U(x0,y1,N+2-k)=w01; 169 | end 170 | if and(x1<=N+1,y0<=N+1) 171 | U(x1,y0,N+2-k)=w10; 172 | end 173 | if and(x1<=N+1,y1<=N+1) 174 | U(x1,y1,N+2-k)=w11; 175 | end 176 | 177 | end 178 | 179 | H=U; 180 | 181 | case 'square' 182 | 183 | end 184 | 185 | return 186 | 187 | 188 | -------------------------------------------------------------------------------- /+biqaa/renyientropy.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dsoellinger/blind_image_quality_toolbox/4d12c43c77bba538f684df0b62621e9350854c43/+biqaa/renyientropy.m -------------------------------------------------------------------------------- /+biqi/biqi.m: -------------------------------------------------------------------------------- 1 | function [quality probs] = biqi(im) 2 | 3 | 4 | %======================================================================== 5 | % 6 | % -----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------ 7 | % Copyright (c) 2009 The University of Texas at Austin 8 | % All rights reserved. 9 | % 10 | % Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, 11 | % modify, and distribute this code (the source files) and its documentation for 12 | % any purpose, provided that the copyright notice in its entirety appear in all copies of this code, and the 13 | % original source of this code, Laboratory for Image and Video Engineering (LIVE, http://live.ece.utexas.edu) 14 | % and Center for Perceptual Systems (CPS, http://www.cps.utexas.edu) at the University of Texas at Austin (UT Austin, 15 | % http://www.utexas.edu), is acknowledged in any publication that reports research using this code. The research 16 | % is to be cited in the bibliography as: 17 | % 18 | % 1. A. K. Moorthy and A. C. Bovik, "A Modular Framework for Constructing Blind 19 | % Universal Quality Indices", submitted to IEEE Signal Processing Letters (2009). 20 | % 21 | % 2. A. K. Moorthy and A. C. Bovik, "BIQI Software Release", 22 | % URL: http://live.ece.utexas.edu/research/quality/biqi.zip, 2009. 23 | % 24 | % IN NO EVENT SHALL THE UNIVERSITY OF TEXAS AT AUSTIN BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, 25 | % OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THIS DATABASE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF TEXAS 26 | % AT AUSTIN HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 27 | % 28 | % THE UNIVERSITY OF TEXAS AT AUSTIN SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 29 | % WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, 30 | % AND THE UNIVERSITY OF TEXAS AT AUSTIN HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. 31 | % 32 | % -----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------% 33 | 34 | %Author : Anush Krishna Moorthy 35 | %Version : 1.0 36 | % 37 | %The authors are with the Laboratory for Image and Video Engineering 38 | %(LIVE), Department of Electrical and Computer Engineering, The 39 | %University of Texas at Austin, Austin, TX. 40 | % 41 | %Kindly report any suggestions or corrections to anushmoorthy@gmail.com 42 | % 43 | %======================================================================== 44 | % 45 | % This is a demonstration of the Blind Image Quality Index (BIQI) . 46 | % It is an implementation of the BIQI in the reference. 47 | % The algorithm is described in: 48 | % A. K. Moorthy and A. C. Bovik, "A Modular Framework for Constructing Blind 49 | % Universal Quality Indices", submitted to IEEE Signal Processing Letters (2009). 50 | % 51 | %You can change this program as you like and use it anywhere, but please 52 | %refer to its original source (cite our paper and our web page at 53 | % http://live.ece.utexas.edu/research/quality/biqi.zip). 54 | % 55 | %Input : A test 8bits/pixel grayscale image loaded in a 2-D array 56 | %Output: A quality score of the image. The score typically has a value 57 | % between 0 and 100 (0 represents the best quality, 100 the worst). 58 | % 59 | %Usage: 60 | % 61 | %1. Load the image, for example 62 | % 63 | % image = rgb2gray(imread('testimage.jpg')); 64 | % 65 | %2. Call this function to calculate the quality score: 66 | % 67 | % quality = biqi(image) 68 | % 69 | % Dependencies: 70 | % MATLAB Wavelet Toolbox 71 | % You may need the MATLAB Image Processing Toolbox 72 | % Binaries: svm-train, svm-scale (from LibSVM) - provided with release 73 | % Other m files: jpeg_quality_score.m (provided with release) 74 | % Data files: range2, range2_wn, range2_blur, range2_jp2k, model_89, 75 | % model_89_wn, model_89_blur, model_89_jp2k, rang2_ff model_ff 76 | %======================================================================== 77 | 78 | 79 | import biqi.* 80 | 81 | 82 | if(size(im,3)~=1) 83 | im = rgb2gray(im); 84 | end 85 | 86 | %% First compute statistics 87 | num_scales = 3; % somethings are hardcoded for this...please be careful when changing. 88 | gam = 0.2:0.001:10; 89 | r_gam = gamma(1./gam).*gamma(3./gam)./(gamma(2./gam)).^2; 90 | 91 | [C S] = wavedec2(im,num_scales,'db9'); 92 | for p = 1:num_scales 93 | [horz_temp,vert_temp,diag_temp] = detcoef2('all',C,S,p) ; 94 | horz(p) = {[horz_temp(:)]}; 95 | diag(p) = {[diag_temp(:)]}; 96 | vert(p) = {[vert_temp(:)]}; 97 | 98 | h_horz_curr = cell2mat(horz(p)); 99 | h_vert_curr = cell2mat(vert(p)); 100 | h_diag_curr = cell2mat(diag(p)); 101 | 102 | mu_horz(p) = mean(h_horz_curr); 103 | sigma_sq_horz(p) = mean((h_horz_curr-mu_horz(p)).^2); 104 | E_horz = mean(abs(h_horz_curr-mu_horz(p))); 105 | rho_horz = sigma_sq_horz(p)/E_horz^2; 106 | [min_difference, array_position] = min(abs(rho_horz - r_gam)); 107 | gam_horz(p) = gam(array_position); 108 | 109 | mu_vert(p) = mean(h_vert_curr); 110 | sigma_sq_vert(p) = mean((h_vert_curr-mu_vert(p)).^2); 111 | E_vert = mean(abs(h_vert_curr-mu_vert(p))); 112 | rho_vert = sigma_sq_vert(p)/E_vert^2; 113 | [min_difference, array_position] = min(abs(rho_vert - r_gam)); 114 | gam_vert(p) = gam(array_position); 115 | 116 | mu_diag(p) = mean(h_diag_curr); 117 | sigma_sq_diag(p) = mean((h_diag_curr-mu_diag(p)).^2); 118 | E_diag = mean(abs(h_diag_curr-mu_diag(p))); 119 | rho_diag = sigma_sq_diag(p)/E_diag^2; 120 | [min_difference, array_position] = min(abs(rho_diag - r_gam)); 121 | gam_diag(p) = gam(array_position); 122 | end 123 | rep_vec = [mu_horz mu_vert mu_diag sigma_sq_horz sigma_sq_vert sigma_sq_diag gam_horz gam_vert gam_diag]; 124 | rep_vec(:,1:9) = []; % remove the means... 125 | %% Now classify 126 | 127 | fid = fopen('test_ind.txt','w') 128 | for j = 1:size(rep_vec,1) 129 | fprintf(fid,'%d ',j); 130 | for k = 1:size(rep_vec,2) 131 | fprintf(fid,'%d:%f ',k,rep_vec(j,k)); 132 | end 133 | fprintf(fid,'\n'); 134 | end 135 | fclose(fid); 136 | 137 | system(['/usr/local/Cellar/libsvm/3.22/bin/svm-scale -r +biqi/range2 test_ind.txt >> test_ind_scaled']); 138 | system(['/usr/local/Cellar/libsvm/3.22/bin/svm-predict -b 1 test_ind_scaled +biqi/model_89 output_89']); 139 | delete test_ind.txt test_ind_scaled 140 | 141 | %% Quality along each dimension 142 | 143 | % Write out SVM compatible 144 | 145 | fid = fopen('test_ind.txt','w'); 146 | for j = 1:size(rep_vec,1) 147 | fprintf(fid,'%f ',j); 148 | for k = 1:size(rep_vec,2) 149 | fprintf(fid,'%d:%f ',k,rep_vec(j,k)); 150 | end 151 | fprintf(fid,'\n'); 152 | end 153 | fclose(fid); 154 | 155 | % Jp2k quality 156 | system(['/usr/local/Cellar/libsvm/3.22/bin/svm-scale -r +biqi/range2_jp2k test_ind.txt >> test_ind_scaled']); 157 | system(['/usr/local/Cellar/libsvm/3.22/bin/svm-predict -b 1 test_ind_scaled +biqi/model_89_jp2k output_blur']); 158 | load output_blur 159 | jp2k_score = output_blur; 160 | delete output_blur test_ind_scaled 161 | 162 | % JPEG quality 163 | jpeg_score = jpeg_quality_score(im); 164 | 165 | 166 | % WN quality 167 | system(['/usr/local/Cellar/libsvm/3.22/bin/svm-scale -r +biqi/range2_wn test_ind.txt >> test_ind_scaled']); 168 | system(['/usr/local/Cellar/libsvm/3.22/bin/svm-predict -b 1 test_ind_scaled +biqi/model_89_wn output_blur']); 169 | load output_blur 170 | wn_score = output_blur; 171 | delete output_blur test_ind_scaled 172 | 173 | 174 | % Blur quality 175 | system(['/usr/local/Cellar/libsvm/3.22/bin/svm-scale -r +biqi/range2_blur test_ind.txt >> test_ind_scaled']); 176 | system(['/usr/local/Cellar/libsvm/3.22/bin/svm-predict -b 1 test_ind_scaled +biqi/model_89_blur output_blur']); 177 | load output_blur 178 | blur_score = output_blur; 179 | delete output_blur test_ind_scaled 180 | 181 | % FF quality 182 | system(['/usr/local/Cellar/libsvm/3.22/bin/svm-scale -r +biqi/range2_ff test_ind.txt >> test_ind_scaled']); 183 | system(['/usr/local/Cellar/libsvm/3.22/bin/svm-predict -b 1 test_ind_scaled +biqi/model_89_ff output_blur']); 184 | load output_blur 185 | ff_score = output_blur; 186 | 187 | 188 | delete output_blur 189 | delete test_ind.txt test_ind_scaled 190 | 191 | 192 | %% Final pooling 193 | 194 | % figure out probabilities 195 | fid = fopen('output_89','r'); 196 | fgetl(fid); 197 | C = textscan(fid,'%f %f %f %f %f %f'); 198 | output = [C{1} C{2} C{3} C{4} C{5} C{6}]; 199 | fclose(fid); 200 | probs = output(:,2:end); 201 | scores = [jp2k_score jpeg_score wn_score blur_score ff_score]; 202 | quality = sum(probs.*scores,2); 203 | delete output_89 204 | clc 205 | 206 | -------------------------------------------------------------------------------- /+biqi/jpeg_quality_score.m: -------------------------------------------------------------------------------- 1 | function [score B A Z] = jpeg_quality_score(img) 2 | 3 | %======================================================================== 4 | % 5 | %Copyright (c) 2002 The University of Texas at Austin 6 | %All Rights Reserved. 7 | % 8 | %This program is free software; you can redistribute it and/or modify 9 | %it under the terms of the GNU General Public License as published by 10 | %the Free Software Foundation; either version 2 of the License, or 11 | %(at your option) any later version. 12 | % 13 | %This program is distributed in the hope that it will be useful, 14 | %but WITHOUT ANY WARRANTY; without even the implied warranty of 15 | %MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 16 | %GNU General Public License for more details. 17 | % 18 | %The GNU Public License is available in the file LICENSE, or you 19 | %can write to the Free Software Foundation, Inc., 59 Temple Place - 20 | %Suite 330, Boston, MA 02111-1307, USA, or you can find it on the 21 | %World Wide Web at http://www.fsf.org. 22 | % 23 | % Author : Zhou Wang 24 | % Version : 1.0 25 | % Modified: Anush Krishna Moorthy 26 | % Version: 1.BUQI 27 | % 28 | %The authors are with the Laboratory for Image and Video Engineering 29 | %(LIVE), Department of Electrical and Computer Engineering, The 30 | %University of Texas at Austin, Austin, TX. 31 | % 32 | %Kindly report any suggestions or corrections to zhouwang@ieee.org 33 | % 34 | %======================================================================== 35 | % 36 | %This is an implementation of the algorithm for calculating the quality 37 | %score of JPEG compressed images proposed by Zhou Wang, Hamid R. Sheikh 38 | %and Alan C. Bovik. Please refer to the paper: Zhou Wang, Hamid R. Sheikh 39 | %and Alan C. Bovik, "No-Reference Perceptual Quality Assessment of JPEG 40 | %Compressed Images," submitted to IEEE International Conference on Image 41 | %Processing, Sept. 2002. 42 | % 43 | %You can change this program as you like and use it anywhere, but please 44 | %refer to its original source (cite our paper and our web page at 45 | %http://anchovy.ece.utexas.edu/~zwang/research/nr_jpeg_quality/index.html). 46 | % 47 | %Input : A test 8bits/pixel grayscale image loaded in a 2-D array 48 | %Output: A quality score of the image. The score typically has a value 49 | % between 1 and 10 (10 represents the best quality, 1 the worst). 50 | % 51 | %Usage: 52 | % 53 | %1. Load the image, for example 54 | % 55 | % image = imread('testimage.jpg'); 56 | % 57 | %2. Call this function to calculate the quality score: 58 | % 59 | % Quality_Score = jpeg_quality_score(image) 60 | % 61 | %======================================================================== 62 | 63 | if (nargin > 1) 64 | score = -1; 65 | return; 66 | end 67 | 68 | [M N] = size(img); 69 | if (M < 16 | N < 16) 70 | score = -2; 71 | return; 72 | end 73 | 74 | x = double(img); 75 | 76 | % Feature Extraction: 77 | 78 | % 1. horizontal features 79 | 80 | d_h = x(:, 2:N) - x(:, 1:(N-1)); 81 | 82 | B_h = mean2(abs(d_h(:, 8:8:8*(floor(N/8)-1)))); 83 | 84 | A_h = (8*mean2(abs(d_h)) - B_h)/7; 85 | 86 | sig_h = sign(d_h); 87 | left_sig = sig_h(:, 1:(N-2)); 88 | right_sig = sig_h(:, 2:(N-1)); 89 | Z_h = mean2((left_sig.*right_sig)<0); 90 | 91 | % 2. vertical features 92 | 93 | d_v = x(2:M, :) - x(1:(M-1), :); 94 | 95 | B_v = mean2(abs(d_v(8:8:8*(floor(M/8)-1), :))); 96 | 97 | A_v = (8*mean2(abs(d_v)) - B_v)/7; 98 | 99 | sig_v = sign(d_v); 100 | up_sig = sig_v(1:(M-2), :); 101 | down_sig = sig_v(2:(M-1), :); 102 | Z_v = mean2((up_sig.*down_sig)<0); 103 | 104 | % 3. combined features 105 | 106 | B = (B_h + B_v)/2; 107 | A = (A_h + A_v)/2; 108 | Z = (Z_h + Z_v)/2; 109 | 110 | % Quality Prediction 111 | 112 | alpha = -927.4240; beta = 850.8986;gamma1 =235.4451 ;gamma2 = 128.7548;gamma3 =-341.4790; 113 | score = alpha + beta*(B.^(gamma1/10000))*(A.^(gamma2/10000))*(Z.^(gamma3/10000)); 114 | -------------------------------------------------------------------------------- /+biqi/model_89_blur: -------------------------------------------------------------------------------- 1 | svm_type nu_svr 2 | kernel_type rbf 3 | gamma 0.0555556 4 | nr_class 2 5 | total_sv 96 6 | rho -46.9972 7 | probA 4.66293 8 | SV 9 | -512 1:-0.995657 2:-0.970916 3:-0.768967 4:-0.994663 5:-0.974861 6:-0.74229 7:-0.998563 8:-0.998879 9:-0.893319 10:-0.783924 11:-0.558299 12:-0.315068 13:-0.78169 14:-0.454023 15:-0.492823 16:0.486367 17:-0.498489 18:-0.836145 10 | 512 1:-0.998568 2:-0.996637 3:-0.884807 4:-0.998667 5:-0.99597 6:-0.926631 7:-1 8:-1 9:-0.999838 10:-0.78911 11:-1 12:-0.932752 13:-0.852113 14:-1 15:-0.956938 16:-0.709575 17:-0.318429 18:-0.783133 11 | -512 1:-0.85734 2:-0.512494 3:-0.0224884 4:-0.688618 5:-0.14185 6:0.399577 7:-0.991004 8:-0.844808 9:-0.507614 10:-0.313742 11:0.102881 12:-0.118306 13:-0.392606 14:0.272989 15:-0.00239234 16:-0.235257 17:-0.448943 18:-0.188755 12 | 512 1:-0.999987 2:-0.999857 3:-0.998634 4:-0.999898 5:-0.999778 6:-0.997894 7:-0.999656 8:-0.999934 9:-0.999999 10:0.574762 11:-0.303155 12:-0.950187 13:0.165493 14:-0.477011 15:-0.820574 16:-0.24033 17:0.300302 18:0.450602 13 | -121.7161536941343 1:-0.967782 2:-0.902319 3:-0.91725 4:-0.973475 5:-0.926182 6:-0.903734 7:-0.995779 8:-0.936469 9:-0.826011 10:-0.856525 11:-0.99177 12:-0.815691 13:-0.697183 14:-0.563218 15:-0.5 16:-0.310082 17:-1 18:-1 14 | -512 1:-0.690164 2:-0.131023 3:0.75344 4:-0.880569 5:-0.735927 6:-0.0868924 7:-0.994107 8:-0.937392 9:-0.807458 10:-0.374244 11:0.377229 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13:-0.407625 14:-0.330539 15:-0.180412 16:-0.516151 17:-0.344183 18:-0.216049 102 | 512 1:-0.898503 2:-0.823477 3:-0.725931 4:-0.91626 5:-0.842243 6:-0.812943 7:-0.925725 8:-0.913124 9:-0.838694 10:-0.21025 11:-0.507066 12:-0.695214 13:0.00527859 14:-0.585629 15:-0.838918 16:0.18488 17:-0.0753466 18:-0.423457 103 | 512 1:-0.92202 2:-0.729158 3:-0.641323 4:-0.940644 5:-0.694547 6:-0.595954 7:-0.976028 8:-0.859135 9:-0.604623 10:-0.807804 11:-0.615602 12:-0.52267 13:-0.612903 14:-0.543713 15:-0.306701 16:-0.697595 17:-0.626281 18:-0.416049 104 | 45.95920928041748 1:-0.66162 2:-0.0232414 3:0.699576 4:-0.763062 5:-0.606355 6:-0.0860491 7:-0.785232 8:-0.788531 9:-0.70459 10:0.308096 11:-0.085359 12:-0.164987 13:0.439296 14:-0.28024 15:-0.775773 16:0.641237 17:0.622664 18:0.388889 105 | 396.456787346148 1:0.626522 2:0.621495 3:-0.555437 4:0.626554 5:0.588848 6:-0.383383 7:0.645265 8:0.604405 9:0.650789 10:0.945253 11:0.932165 12:0.832494 13:0.939003 14:0.913772 15:0.970361 16:0.942268 17:0.907173 18:0.834568 106 | -------------------------------------------------------------------------------- /+biqi/range2: -------------------------------------------------------------------------------- 1 | x 2 | -1 1 3 | 1 0.08709400000000001 6300.437869 4 | 2 0.113197 6472.083419 5 | 3 1.263355 23954.455546 6 | 4 0.079068 6305.392224 7 | 5 0.132499 6598.009142 8 | 6 1.253984 18802.900041 9 | 7 0.046982 6268.932425 10 | 8 0.060238 6194.020033 11 | 9 0.130056 6274.840277 12 | 10 0.2 2.116 13 | 11 0.2 2.027 14 | 12 0.2 1.864 15 | 13 0.2 2.129 16 | 14 0.2 2.018 17 | 15 0.2 1.865 18 | 16 0.2 2.122 19 | 17 0.2 1.991 20 | 18 0.2 1.919 21 | -------------------------------------------------------------------------------- /+biqi/range2_blur: -------------------------------------------------------------------------------- 1 | x 2 | -1 1 3 | 1 0.106397 416.792733 4 | 2 0.294252 3418.638564 5 | 3 8.300639 22148.980534 6 | 4 0.08538 322.135437 7 | 5 0.54513 3243.785652 8 | 6 20.29214 16531.137122 9 | 7 0.046982 71.08415599999999 10 | 8 0.060238 785.4126 11 | 9 0.160248 3653.684223 12 | 10 0.216 1.373 13 | 11 0.271 1 14 | 12 0.277 1.08 15 | 13 0.2 1.336 16 | 14 0.201 0.897 17 | 15 0.247 1.083 18 | 16 0.443 2.02 19 | 17 0.283 1.938 20 | 18 0.287 1.532 21 | -------------------------------------------------------------------------------- /+biqi/range2_ff: -------------------------------------------------------------------------------- 1 | x 2 | -1 1 3 | 1 0.08709400000000001 628.7151290000001 4 | 2 0.113197 4606.107029 5 | 3 1.263355 23824.564716 6 | 4 0.079068 643.821223 7 | 5 0.132499 4432.316677 8 | 6 1.253984 18113.519251 9 | 7 0.067426 87.11413 10 | 8 0.082077 975.756217 11 | 9 0.130056 4285.781159 12 | 10 0.2 1.808 13 | 11 0.218 1.468 14 | 12 0.221 1.199 15 | 13 0.2 1.852 16 | 14 0.239 1.381 17 | 15 0.2 1.102 18 | 16 0.2 1.974 19 | 17 0.205 1.862 20 | 18 0.2 1.644 21 | -------------------------------------------------------------------------------- /+biqi/range2_jp2k: -------------------------------------------------------------------------------- 1 | x 2 | -1 1 3 | 1 0.415697 651.7276440000001 4 | 2 7.920673 4712.351686 5 | 3 487.872056 23954.455546 6 | 4 0.589105 659.673711 7 | 5 10.202901 4460.99447 8 | 6 364.64863 18181.730394 9 | 7 0.077676 117.857701 10 | 8 0.211122 1046.294556 11 | 9 52.023136 4405.805863 12 | 10 0.2 0.89 13 | 11 0.2 0.903 14 | 12 0.2 1.082 15 | 13 0.2 0.775 16 | 14 0.2 0.869 17 | 15 0.23 1.1 18 | 16 0.2 1.353 19 | 17 0.2 0.998 20 | 18 0.2 1.07 21 | -------------------------------------------------------------------------------- /+biqi/range2_wn: -------------------------------------------------------------------------------- 1 | x 2 | -1 1 3 | 1 32.923264 6300.437869 4 | 2 310.022219 6472.083419 5 | 3 1004.673879 23850.493681 6 | 4 25.888281 6305.392224 7 | 5 139.40511 6598.009142 8 | 6 559.452918 17771.879557 9 | 7 10.229548 6268.932425 10 | 8 90.96772799999999 6194.020033 11 | 9 245.006759 6274.840277 12 | 10 0.399 2.116 13 | 11 0.258 2.027 14 | 12 0.276 1.864 15 | 13 0.424 2.129 16 | 14 0.348 2.018 17 | 15 0.313 1.865 18 | 16 0.667 2.122 19 | 17 0.332 1.991 20 | 18 0.299 1.919 21 | -------------------------------------------------------------------------------- /+biqi/readme.txt: -------------------------------------------------------------------------------- 1 | BIQI Software release. 2 | 3 | 4 | ======================================================================== 5 | 6 | -----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------ 7 | Copyright (c) 2009 The University of Texas at Austin 8 | All rights reserved. 9 | 10 | Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, 11 | modify, and distribute this code (the source files) and its documentation for 12 | any purpose, provided that the copyright notice in its entirety appear in all copies of this code, and the 13 | original source of this code, Laboratory for Image and Video Engineering (LIVE, http://live.ece.utexas.edu) 14 | and Center for Perceptual Systems (CPS, http://www.cps.utexas.edu) at the University of Texas at Austin (UT Austin, 15 | http://www.utexas.edu), is acknowledged in any publication that reports research using this code. The research 16 | is to be cited in the bibliography as: 17 | 18 | 1. A. K. Moorthy and A. C. Bovik, "A Modular Framework for Constructing Blind 19 | Universal Quality Indices", submitted to IEEE Signal Processing Letters (2009). 20 | 21 | 2. A. K. Moorthy and A. C. Bovik, "BIQI Software Release", 22 | URL: http://live.ece.utexas.edu/research/quality/biqi.zip, 2009. 23 | 24 | IN NO EVENT SHALL THE UNIVERSITY OF TEXAS AT AUSTIN BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, 25 | OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THIS DATABASE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF TEXAS 26 | AT AUSTIN HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 27 | 28 | THE UNIVERSITY OF TEXAS AT AUSTIN SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 29 | WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, 30 | AND THE UNIVERSITY OF TEXAS AT AUSTIN HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. 31 | 32 | -----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------% 33 | 34 | Author : Anush Krishna Moorthy 35 | Version : 1.0 36 | 37 | The authors are with the Laboratory for Image and Video Engineering 38 | (LIVE), Department of Electrical and Computer Engineering, The 39 | University of Texas at Austin, Austin, TX. 40 | 41 | Kindly report any suggestions or corrections to anushmoorthy@gmail.com 42 | 43 | ======================================================================== 44 | 45 | This is a demonstration of the Blind Image Quality Index (BIQI). 46 | It is an implementation of the BIQI in the reference. 47 | The algorithm is described in: 48 | A. K. Moorthy and A. C. Bovik, "A Modular Framework for Constructing Blind 49 | Universal Quality Indices", submitted to IEEE Signal Processing Letters (2009). 50 | 51 | You can change this program as you like and use it anywhere, but please 52 | refer to its original source (cite our paper and our web page at 53 | http://live.ece.utexas.edu/research/quality/biqi.zip). 54 | 55 | Input : A test 8bits/pixel grayscale image loaded in a 2-D array 56 | Output: A quality score of the image. The score typically has a value 57 | between 0 and 100 (0 represents the best quality, 100 the worst). 58 | 59 | Usage: 60 | 61 | 1. Load the image, for example 62 | 63 | image = rgb2gray(imread('testimage.jpg')); 64 | 65 | 2. Call this function to calculate the quality score: 66 | 67 | quality = biqi(image) 68 | 69 | Dependencies: 70 | MATLAB Wavelet Toolbox 71 | You may need the MATLAB Image Processing Toolbox 72 | Binaries: svm-train, svm-scale (from LibSVM) - provided with release 73 | Other m files: jpeg_quality_score.m (provided with release) 74 | Data files: range2, range2_wn, range2_blur, range2_jp2k, model_89, 75 | model_89_wn, model_89_blur, model_89_jp2k, range2_ff, model_89_ff (provided with release) 76 | ======================================================================== 77 | 78 | Note on training: 79 | This release version of BIQI was trained on the entire LIVE database. 80 | 81 | 82 | This program uses LibSVM binaries. 83 | Below is the requried copyright notice for the binaries distributed with this release. 84 | 85 | ==================================================================== 86 | LibSVM tools: svm-predict, svm-scale (binaries) 87 | -------------------------------------------------------------------- 88 | 89 | 90 | Copyright (c) 2000-2009 Chih-Chung Chang and Chih-Jen Lin 91 | All rights reserved. 92 | 93 | Redistribution and use in source and binary forms, with or without 94 | modification, are permitted provided that the following conditions 95 | are met: 96 | 97 | 1. Redistributions of source code must retain the above copyright 98 | notice, this list of conditions and the following disclaimer. 99 | 100 | 2. Redistributions in binary form must reproduce the above copyright 101 | notice, this list of conditions and the following disclaimer in the 102 | documentation and/or other materials provided with the distribution. 103 | 104 | 3. Neither name of copyright holders nor the names of its contributors 105 | may be used to endorse or promote products derived from this software 106 | without specific prior written permission. 107 | 108 | 109 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 110 | ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 111 | LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 112 | A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR 113 | CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 114 | EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 115 | PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 116 | PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 117 | LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 118 | NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 119 | SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 120 | ==================================================================== -------------------------------------------------------------------------------- /+bliinds2/bliinds2_feature_extraction.m: -------------------------------------------------------------------------------- 1 | function features = bliinds2_feature_extraction(I) 2 | 3 | import bliinds2.* 4 | 5 | h=fspecial('gaussian',3); 6 | 7 | Img = double(I(:,:,1)); 8 | 9 | coeff_freq_var_L1 = blkproc(Img,[3,3],[1,1],@rho_dct); 10 | gama_L1 = blkproc(Img,[3,3],[1,1],@gama_dct); 11 | ori1_rho_L1 = blkproc(Img,[3 3],[1,1],@oriented1_dct_rho_config3); 12 | ori2_rho_L1 = blkproc(Img,[3 3],[1,1],@oriented2_dct_rho_config3); 13 | ori3_rho_L1 = blkproc(Img,[3 3],[1,1],@oriented3_dct_rho_config3); 14 | subband_energy_L1 = blkproc(Img,[3 3],[1,1],@dct_freq_bands); 15 | 16 | rho_sorted_temp = sort(coeff_freq_var_L1(:),'descend'); 17 | rho_count = length(rho_sorted_temp); 18 | percentile10_coeff_freq_var_L1=mean(rho_sorted_temp(1:ceil(rho_count/10))); 19 | percentile100_coeff_freq_var_L1=mean(rho_sorted_temp(:)); 20 | clear rho_sorted_temp rho_count 21 | 22 | gama_sorted_temp = sort(gama_L1(:),'ascend'); 23 | gama_count = length(gama_sorted_temp); 24 | percentile10_gama_L1=mean(gama_sorted_temp(1:ceil(gama_count/10))); 25 | percentile100_gama_L1=mean(gama_sorted_temp(:)); 26 | clear gama_sorted_temp gama_count 27 | 28 | subband_energy_sorted_temp = sort(subband_energy_L1(:),'descend'); 29 | subband_energy_count = length(subband_energy_sorted_temp); 30 | percentile10_subband_energy_L1=mean(subband_energy_sorted_temp(1:ceil(subband_energy_count/10))); 31 | percentile100_subband_energy_L1=mean(subband_energy_sorted_temp(:)); 32 | clear freq_bands_sorted_temp freq_bands_count 33 | 34 | temp_size=size(ori1_rho_L1); 35 | var_temp=zeros(temp_size); 36 | 37 | for i=1:temp_size(1) 38 | for j=1:temp_size(2) 39 | var_temp(i,j)=var([ori1_rho_L1(i,j) ori2_rho_L1(i,j) ori3_rho_L1(i,j)]); 40 | end 41 | end 42 | ori_rho_L1=var_temp; 43 | 44 | ori_sorted_temp = sort(ori_rho_L1(:),'descend'); 45 | ori_count = length(ori_sorted_temp); 46 | percentile10_orientation_L1=mean(ori_sorted_temp(1:ceil(ori_count/10))); 47 | percentile100_orientation_L1=mean(ori_sorted_temp(:)); 48 | clear var_ori_sorted_temp rho_count 49 | 50 | features_L1 = [percentile100_coeff_freq_var_L1;percentile10_coeff_freq_var_L1;percentile100_gama_L1;percentile10_gama_L1;percentile100_subband_energy_L1;percentile10_subband_energy_L1;percentile100_orientation_L1;percentile10_orientation_L1]; 51 | 52 | %% 53 | 54 | Img1_filtered=double(imfilter(Img,h)); 55 | Img2 = Img1_filtered(2:2:end,2:2:end); 56 | 57 | coeff_freq_var_L2 = blkproc(Img2,[3,3],[1,1],@rho_dct); 58 | gama_L2 = blkproc(Img2,[3,3],[1,1],@gama_dct); 59 | ori1_rho_L2 = blkproc(Img2,[3 3],[1,1],@oriented1_dct_rho_config3); 60 | ori2_rho_L2 = blkproc(Img2,[3 3],[1,1],@oriented2_dct_rho_config3); 61 | ori3_rho_L2 = blkproc(Img2,[3 3],[1,1],@oriented3_dct_rho_config3); 62 | subband_energy_L2 = blkproc(Img2,[3 3],[1,1],@dct_freq_bands); 63 | 64 | rho_sorted_temp = sort(coeff_freq_var_L2(:),'descend'); 65 | rho_count = length(rho_sorted_temp); 66 | percentile10_coeff_freq_var_L2=mean(rho_sorted_temp(1:ceil(rho_count/10))); 67 | percentile100_coeff_freq_var_L2=mean(rho_sorted_temp(:)); 68 | clear rho_sorted_temp rho_count 69 | 70 | gama_sorted_temp = sort(gama_L2(:),'ascend'); 71 | gama_count = length(gama_sorted_temp); 72 | percentile10_gama_L2=mean(gama_sorted_temp(1:ceil(gama_count/10))); 73 | percentile100_gama_L2=mean(gama_sorted_temp(:)); 74 | clear gama_sorted_temp gama_count 75 | 76 | subband_energy_sorted_temp = sort(subband_energy_L2(:),'descend'); 77 | subband_energy_count = length(subband_energy_sorted_temp); 78 | percentile10_subband_energy_L2=mean(subband_energy_sorted_temp(1:ceil(subband_energy_count/10))); 79 | percentile100_subband_energy_L2=mean(subband_energy_sorted_temp(:)); 80 | clear freq_bands_sorted_temp freq_bands_count 81 | 82 | temp_size=size(ori1_rho_L2); 83 | var_temp=zeros(temp_size); 84 | 85 | for i=1:temp_size(1) 86 | for j=1:temp_size(2) 87 | var_temp(i,j)=var([ori1_rho_L2(i,j) ori2_rho_L2(i,j) ori3_rho_L2(i,j)]); 88 | end 89 | end 90 | ori_rho_L2=var_temp; 91 | 92 | ori_sorted_temp = sort(ori_rho_L2(:),'descend'); 93 | ori_count = length(ori_sorted_temp); 94 | percentile10_orientation_L2=mean(ori_sorted_temp(1:ceil(ori_count/10))); 95 | percentile100_orientation_L2=mean(ori_sorted_temp(:)); 96 | clear var_ori_sorted_temp rho_count 97 | 98 | features_L2 = [percentile100_coeff_freq_var_L2;percentile10_coeff_freq_var_L2;percentile100_gama_L2;percentile10_gama_L2;percentile100_subband_energy_L2;percentile10_subband_energy_L2;percentile100_orientation_L2;percentile10_orientation_L2]; 99 | 100 | %% 101 | Img2_filtered=double(imfilter(Img2,h)); 102 | Img3 = Img2_filtered(2:2:end,2:2:end); 103 | 104 | coeff_freq_var_L3 = blkproc(Img3,[3,3],[1,1],@rho_dct); 105 | gama_L3 = blkproc(Img3,[3,3],[1,1],@gama_dct); 106 | ori1_rho_L3 = blkproc(Img3,[3 3],[1,1],@oriented1_dct_rho_config3); 107 | ori2_rho_L3 = blkproc(Img3,[3 3],[1,1],@oriented2_dct_rho_config3); 108 | ori3_rho_L3 = blkproc(Img3,[3 3],[1,1],@oriented3_dct_rho_config3); 109 | subband_energy_L3 = blkproc(Img3,[3 3],[1,1],@dct_freq_bands); 110 | 111 | rho_sorted_temp = sort(coeff_freq_var_L3(:),'descend'); 112 | rho_count = length(rho_sorted_temp); 113 | percentile10_coeff_freq_var_L3=mean(rho_sorted_temp(1:ceil(rho_count/10))); 114 | percentile100_coeff_freq_var_L3=mean(rho_sorted_temp(:)); 115 | clear rho_sorted_temp rho_count 116 | 117 | gama_sorted_temp = sort(gama_L3(:),'ascend'); 118 | gama_count = length(gama_sorted_temp); 119 | percentile10_gama_L3=mean(gama_sorted_temp(1:ceil(gama_count/10))); 120 | percentile100_gama_L3=mean(gama_sorted_temp(:)); 121 | clear gama_sorted_temp gama_count 122 | 123 | subband_energy_sorted_temp = sort(subband_energy_L3(:),'descend'); 124 | subband_energy_count = length(subband_energy_sorted_temp); 125 | percentile10_subband_energy_L3=mean(subband_energy_sorted_temp(1:ceil(subband_energy_count/10))); 126 | percentile100_subband_energy_L3=mean(subband_energy_sorted_temp(:)); 127 | clear freq_bands_sorted_temp freq_bands_count 128 | 129 | temp_size=size(ori1_rho_L3); 130 | var_temp=zeros(temp_size); 131 | 132 | for i=1:temp_size(1) 133 | for j=1:temp_size(2) 134 | var_temp(i,j)=var([ori1_rho_L3(i,j) ori2_rho_L3(i,j) ori3_rho_L3(i,j)]); 135 | end 136 | end 137 | ori_rho_L3=var_temp; 138 | 139 | ori_sorted_temp = sort(ori_rho_L3(:),'descend'); 140 | ori_count = length(ori_sorted_temp); 141 | percentile10_orientation_L3=mean(ori_sorted_temp(1:ceil(ori_count/10))); 142 | percentile100_orientation_L3=mean(ori_sorted_temp(:)); 143 | clear var_ori_sorted_temp rho_count 144 | 145 | features_L3 = [percentile100_coeff_freq_var_L3;percentile10_coeff_freq_var_L3;percentile100_gama_L3;percentile10_gama_L3;percentile100_subband_energy_L3;percentile10_subband_energy_L3;percentile100_orientation_L3;percentile10_orientation_L3]; 146 | 147 | %% 148 | features = [features_L1' features_L2' features_L3']; -------------------------------------------------------------------------------- /+bliinds2/bliinds2_score.m: -------------------------------------------------------------------------------- 1 | function [ score ] = bliinds2_score( image ) 2 | %BLIINDS2_SCORE Summary of this function goes here 3 | % Detailed explanation goes here 4 | 5 | import bliinds2.* 6 | 7 | features = bliinds2_feature_extraction(image); 8 | score = bliinds_prediction(features); 9 | 10 | end 11 | 12 | -------------------------------------------------------------------------------- /+bliinds2/bliinds_prediction.m: -------------------------------------------------------------------------------- 1 | function predicted_score = bliinds_prediction(features) 2 | 3 | 4 | % features: needs to be a ROW vector of features 5 | 6 | b = 1.0168; 7 | gama = 0.4200; 8 | mu = [1.4526,2.6063,1.5189,0.4061,0.7595,1.0661,0.1668,0.5942,1.3617,2.4291,1.359,0.4069,0.7489,1.0642,0.1261,0.4472,1.3117,2.3635,1.2518,0.3962,0.7488,1.0442,0.1111,0.4005,38.4298]; 9 | sigma_inv = 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10 | 11 | iVect=[0:0.5:100]; 12 | 13 | count=0; 14 | for i=1:0.5:100 15 | count=count+1; 16 | p_temp(count)=exp(-(b*(([features i]-mu)*sigma_inv*([features i]-mu)'))^(gama/1)); 17 | end 18 | [ttt IXX]=max(p_temp); 19 | dm=iVect(IXX); 20 | clear p_temp* IXX 21 | 22 | predicted_score=dm; 23 | -------------------------------------------------------------------------------- /+bliinds2/blinds_test.m: -------------------------------------------------------------------------------- 1 | image = double(imread('img3.png')); 2 | 3 | features = bliinds2_feature_extraction(image) 4 | score = bliinds_prediction(features) -------------------------------------------------------------------------------- /+bliinds2/dct_freq_bands.m: -------------------------------------------------------------------------------- 1 | function f = dct_freq_bands(In) 2 | 3 | I=dct2(In); 4 | eps=0.00000001; 5 | 6 | %% 5x5 freq band 1 7 | var_band1=var([I(1,2) I(2,1) I(1,3) I(3,1) I(2,2)]); 8 | 9 | 10 | %% 5x5 freq band 2 11 | var_band2=var([I(4,1) I(5,1) I(3,2) I(4,2) I(5,2) I(2,3) I(3,3) I(4,3) I(1,4) I(2,4) I(3,4) I(1,5) I(2,5)]); 12 | 13 | %% 5x5 freq band 3 14 | var_band3=var([I(3,5) I(4,4) I(5,3) I(4,5) I(5,4) I(5,5)]); 15 | 16 | r1 = abs(var_band3 - mean([var_band1 var_band2]))/(var_band3 + mean([var_band1 var_band2])+eps); 17 | r2 = abs(var_band2 - var_band1)/(var_band3 + var_band1+eps); 18 | 19 | f = (r1+r2)/2; -------------------------------------------------------------------------------- /+bliinds2/fitting.m: -------------------------------------------------------------------------------- 1 | function yhat = fitting(beta,x) 2 | 3 | b1 = beta(1); 4 | b2 = beta(2); 5 | b3 = beta(3); 6 | b4 = beta(4); 7 | b5 = beta(5); 8 | 9 | yhat = b1*(0.5 - 1./(1+exp(b2*(x-b3)))) + b4.*x + b5; 10 | -------------------------------------------------------------------------------- /+bliinds2/gama_dct.m: -------------------------------------------------------------------------------- 1 | function g = gama_dct(I) 2 | 3 | import bliinds2.*; 4 | 5 | temp1=dct2(I); 6 | temp2=temp1(:); 7 | temp3=temp2(2:end); 8 | 9 | %g=kurtosis(temp3); 10 | g=gama_gen_gauss(temp3); -------------------------------------------------------------------------------- /+bliinds2/gama_gen_gauss.m: -------------------------------------------------------------------------------- 1 | function gama =gama_gen_gauss(I) 2 | 3 | mean_gauss=mean(I(:)); 4 | var_gauss=var(I(:)); 5 | mean_abs=mean(abs(I(:)-mean_gauss))^2; 6 | rho=var_gauss/(mean_abs+0.0000001); 7 | 8 | g=[0.03:0.001:10]; 9 | r=gamma(1./g).*gamma(3./g)./(gamma(2./g).^2); 10 | 11 | gamma_gauss=11; 12 | for j=1:length(g)-1 13 | if rho<=r(j) && rho>r(j+1) 14 | %j 15 | gamma_gauss=g(j); 16 | break 17 | end 18 | end 19 | gama=gamma_gauss; 20 | 21 | % %b=(1/sqrt(var_gauss))*sqrt(gamma(3/gama)/gamma(1/gama)); 22 | % %a=b*gama/(2*gamma(1/gama)); -------------------------------------------------------------------------------- /+bliinds2/oriented1_dct_rho_config3.m: -------------------------------------------------------------------------------- 1 | function g1 = oriented1_dct_rho_config3(I) 2 | 3 | %H=fspecial('gaussian',[7 7]); 4 | 5 | temp=dct2(I); 6 | eps=0.00000001; 7 | %% 3x3 8 | % temp1=[temp(1,3) temp(2,3)]; 9 | 10 | 11 | %% 7x7 12 | % temp1=[temp(1,2:end) temp(2,3:end) temp(3,5:end) temp(4,6:end)]; 13 | 14 | 15 | %% 5x5 16 | 17 | F = [0 1 1 1 1;0 0 1 1 1; 0 0 0 0 1; 0 0 0 0 0;0 0 0 0 0]; 18 | temp1=temp(F~=0); 19 | 20 | %% 9x9 21 | % temp1=[temp(1,2:end) temp(2,3:end) temp(3,5:end) temp(4,6:end) temp(5,8:end) temp(6,9:end)]; 22 | 23 | std_gauss=std(abs(temp1(:))); 24 | mean_abs=mean(abs(temp1(:))); 25 | g1=std_gauss/(mean_abs+0.0000001); 26 | 27 | % g1=var(temp1)/mean(temp1); 28 | -------------------------------------------------------------------------------- /+bliinds2/oriented2_dct_rho_config3.m: -------------------------------------------------------------------------------- 1 | function g2 = oriented2_dct_rho_config3(I) 2 | 3 | %H=fspecial('gaussian',[7 7]); 4 | temp=dct2(I); 5 | eps=0.00000001; 6 | %% 3x3 7 | % temp2=[temp(2,2) temp(3,3)]; 8 | 9 | %% 7x7 10 | % temp2=[temp(2,2) temp(3,3) temp(4,4) temp(5,5) temp(6,6) temp(7,7) temp(3,4) temp(4,3) temp(4,5) temp(5,4) temp(5,6) temp(6,5) temp(6,7) temp(7,6) temp(5,7) temp(7,5)]; 11 | 12 | %% 5x5 13 | F = [0 0 0 0 0; 0 1 0 0 0; 0 0 1 1 0; 0 0 1 1 1; 0 0 0 1 1]; 14 | temp2=temp(F~=0); 15 | 16 | %% 9x9 17 | % temp2=[temp(2,2) temp(3,3) temp(4,4) temp(5,5) temp(6,6) temp(7,7) temp(8,8) temp(9,9) temp(3,4) temp(4,3) temp(4,5) temp(5,4) temp(5,6) temp(6,5) temp(6,7) temp(7,6) temp(5,7) temp(7,5) temp(6,8) temp(8,6) temp(7,8) temp(8,7) temp(7,9) temp(9,7) temp(8,9) temp(9,8)]; 18 | 19 | 20 | std_gauss=std(abs(temp2(:))); 21 | mean_abs=mean(abs(temp2(:))); 22 | g2=std_gauss/(mean_abs+0.0000001); 23 | 24 | % g2=var(temp2)/(mean(temp2)+eps); -------------------------------------------------------------------------------- /+bliinds2/oriented3_dct_rho_config3.m: -------------------------------------------------------------------------------- 1 | function g3 = oriented3_dct_rho_config3(I) 2 | 3 | %H=fspecial('gaussian',[7 7]); 4 | 5 | eps=0.00000001; 6 | temp=dct2(I); 7 | 8 | %% 3x3 9 | % temp3=[temp(2,1) temp(3,1) temp(3,2)]; 10 | 11 | %% 7x7 12 | % temp3=[temp(2:end,1)' temp(3:end,2)' temp(5:end,3)' temp(6:end,4)']; 13 | 14 | %% 5x5 15 | F = [0 1 1 1 1;0 0 1 1 1; 0 0 0 0 1; 0 0 0 0 0;0 0 0 0 0]'; 16 | temp3=temp(F~=0); 17 | 18 | %% 9x9 19 | % temp3=[temp(2:end,1)' temp(3:end,2)' temp(5:end,3)' temp(6:end,4)' temp(8:end,5)' temp(9:end,6)']; 20 | 21 | std_gauss=std(abs(temp3(:))); 22 | mean_abs=mean(abs(temp3(:))); 23 | g3=std_gauss/(mean_abs+0.0000001); 24 | 25 | % g3=var(temp3)/mean(temp3); -------------------------------------------------------------------------------- /+bliinds2/parameters_from_training.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dsoellinger/blind_image_quality_toolbox/4d12c43c77bba538f684df0b62621e9350854c43/+bliinds2/parameters_from_training.mat -------------------------------------------------------------------------------- /+bliinds2/rho_dct.m: -------------------------------------------------------------------------------- 1 | function g = rho_dct(I) 2 | 3 | import bliinds2.*; 4 | 5 | temp1=dct2(I); 6 | temp2=temp1(:); 7 | temp3=temp2(2:end); 8 | 9 | %g=kurtosis(temp3); 10 | g= rho_gen_gauss(temp3); -------------------------------------------------------------------------------- /+bliinds2/rho_gen_gauss.m: -------------------------------------------------------------------------------- 1 | function rho =rho_gen_gauss(I) 2 | %% we know this one works well 3 | % mean_gauss=mean(I(:)); 4 | % var_gauss=var(I(:)); 5 | % mean_abs=mean(abs(I(:)-mean_gauss))^2; 6 | % rho=var_gauss/(mean_abs+0.0000001); 7 | 8 | %% let's see how well this one does 9 | 10 | % mean_gauss=mean(I(:)); 11 | % std_gauss=std(abs(I(:))); 12 | % mean_abs=mean(abs(I(:)-mean_gauss)); 13 | % rho=std_gauss/(mean_abs+0.0000001); 14 | 15 | %% Third thing to test 16 | 17 | std_gauss=std(abs(I(:))); 18 | mean_abs=mean(abs(I(:))); 19 | rho=std_gauss/(mean_abs+0.0000001); 20 | -------------------------------------------------------------------------------- /+brisque/allrange: -------------------------------------------------------------------------------- 1 | x 2 | -1 1 3 | 1 0.338 10 4 | 2 0.017204 0.806612 5 | 3 0.236 1.642 6 | 4 -0.123884 0.20293 7 | 5 0.000155 0.712298 8 | 6 0.001122 0.470257 9 | 7 0.244 1.641 10 | 8 -0.123586 0.179083 11 | 9 0.000152 0.710456 12 | 10 0.000975 0.470984 13 | 11 0.249 1.555 14 | 12 -0.135687 0.100858 15 | 13 0.000174 0.684173 16 | 14 0.000913 0.534174 17 | 15 0.258 1.561 18 | 16 -0.143408 0.100486 19 | 17 0.000179 0.685696 20 | 18 0.000888 0.536508 21 | 19 0.471 3.264 22 | 20 0.012809 0.703171 23 | 21 0.218 1.046 24 | 22 -0.094876 0.187459 25 | 23 1.5e-005 0.442057 26 | 24 0.001272 0.40803 27 | 25 0.222 1.042 28 | 26 -0.115772 0.162604 29 | 27 1.6e-005 0.444362 30 | 28 0.001374 0.40243 31 | 29 0.227 0.996 32 | 30 -0.117188 0.09832299999999999 33 | 31 3e-005 0.531903 34 | 32 0.001122 0.369589 35 | 33 0.228 0.99 36 | 34 -0.12243 0.098658 37 | 35 2.8e-005 0.530092 38 | 36 0.001118 0.370399 39 | -------------------------------------------------------------------------------- /+brisque/brisque_feature.m: -------------------------------------------------------------------------------- 1 | function feat = brisque_feature(imdist) 2 | 3 | import brisque.*; 4 | 5 | %------------------------------------------------ 6 | % Feature Computation 7 | %------------------------------------------------- 8 | scalenum = 2; 9 | window = fspecial('gaussian',7,7/6); 10 | window = window/sum(sum(window)); 11 | 12 | feat = []; 13 | tic 14 | for itr_scale = 1:scalenum 15 | 16 | mu = filter2(window, imdist, 'same'); 17 | mu_sq = mu.*mu; 18 | sigma = sqrt(abs(filter2(window, imdist.*imdist, 'same') - mu_sq)); 19 | structdis = (imdist-mu)./(sigma+1); 20 | 21 | 22 | [alpha overallstd] = estimateggdparam(structdis(:)); 23 | feat = [feat alpha overallstd^2]; 24 | 25 | 26 | shifts = [ 0 1;1 0 ; 1 1; -1 1]; 27 | 28 | for itr_shift =1:4 29 | 30 | shifted_structdis = circshift(structdis,shifts(itr_shift,:)); 31 | pair = structdis(:).*shifted_structdis(:); 32 | [alpha leftstd rightstd] = estimateaggdparam(pair); 33 | const =(sqrt(gamma(1/alpha))/sqrt(gamma(3/alpha))); 34 | meanparam =(rightstd-leftstd)*(gamma(2/alpha)/gamma(1/alpha))*const; 35 | feat =[feat alpha meanparam leftstd^2 rightstd^2]; 36 | 37 | end 38 | 39 | 40 | imdist = imresize(imdist,0.5); 41 | 42 | 43 | end 44 | toc -------------------------------------------------------------------------------- /+brisque/brisquescore.m: -------------------------------------------------------------------------------- 1 | function qualityscore = brisquescore(imdist) 2 | 3 | import brisque.*; 4 | 5 | if(size(imdist,3)==3) 6 | imdist = uint8(imdist); 7 | imdist = rgb2gray(imdist); 8 | end 9 | 10 | imdist = double(imdist); 11 | 12 | if(nargin<2) 13 | feat = brisque_feature(imdist); 14 | disp('feat computed') 15 | end 16 | 17 | 18 | %--------------------------------------------------------------------- 19 | %Quality Score Computation 20 | %--------------------------------------------------------------------- 21 | 22 | 23 | fid = fopen('test_ind','w'); 24 | 25 | for jj = 1:size(feat,1) 26 | 27 | fprintf(fid,'1 '); 28 | for kk = 1:size(feat,2) 29 | fprintf(fid,'%d:%f ',kk,feat(jj,kk)); 30 | end 31 | fprintf(fid,'\n'); 32 | end 33 | 34 | fclose(fid); 35 | warning off all 36 | delete output test_ind_scaled dump 37 | system('svm-scale -r +brisque/allrange test_ind >> test_ind_scaled'); 38 | system('svm-predict -b 1 test_ind_scaled +brisque/allmodel output >>dump'); 39 | 40 | load output 41 | qualityscore = output; 42 | 43 | delete output test_ind_scaled dump test_ind 44 | -------------------------------------------------------------------------------- /+brisque/estimateaggdparam.m: -------------------------------------------------------------------------------- 1 | function [alpha leftstd rightstd] = estimateaggdparam(vec) 2 | 3 | 4 | gam = 0.2:0.001:10; 5 | r_gam = ((gamma(2./gam)).^2)./(gamma(1./gam).*gamma(3./gam)); 6 | 7 | 8 | leftstd = sqrt(mean((vec(vec<0)).^2)); 9 | rightstd = sqrt(mean((vec(vec>0)).^2)); 10 | gammahat = leftstd/rightstd; 11 | rhat = (mean(abs(vec)))^2/mean((vec).^2); 12 | rhatnorm = (rhat*(gammahat^3 +1)*(gammahat+1))/((gammahat^2 +1)^2); 13 | [min_difference, array_position] = min((r_gam - rhatnorm).^2); 14 | alpha = gam(array_position); 15 | 16 | 17 | -------------------------------------------------------------------------------- /+brisque/estimateggdparam.m: -------------------------------------------------------------------------------- 1 | function [gamparam sigma] = estimateggdparam(vec) 2 | 3 | 4 | 5 | gam = 0.2:0.001:10; 6 | r_gam = (gamma(1./gam).*gamma(3./gam))./((gamma(2./gam)).^2); 7 | 8 | sigma_sq = mean((vec).^2); 9 | sigma = sqrt(sigma_sq); 10 | E = mean(abs(vec)); 11 | rho = sigma_sq/E^2; 12 | [min_difference, array_position] = min(abs(rho - r_gam)); 13 | gamparam = gam(array_position); 14 | 15 | 16 | 17 | 18 | 19 | 20 | -------------------------------------------------------------------------------- /+brisque/readme.txt: -------------------------------------------------------------------------------- 1 | RISQUEE Software release. 2 | 3 | 4 | ======================================================================== 5 | 6 | -----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------ 7 | Copyright (c) 2011 The University of Texas at Austin 8 | All rights reserved. 9 | 10 | Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, 11 | modify, and distribute this code (the source files) and its documentation for 12 | any purpose, provided that the copyright notice in its entirety appear in all copies of this code, and the 13 | original source of this code, Laboratory for Image and Video Engineering (LIVE, http://live.ece.utexas.edu) 14 | and Center for Perceptual Systems (CPS, http://www.cps.utexas.edu) at the University of Texas at Austin (UT Austin, 15 | http://www.utexas.edu), is acknowledged in any publication that reports research using this code. The research 16 | is to be cited in the bibliography as: 17 | 18 | 1) A. Mittal, A. K. Moorthy and A. C. Bovik, "BRISQUE Software Release", 19 | URL: http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip, 2011 20 | 21 | 2) A. Mittal, A. K. Moorthy and A. C. Bovik, "No Reference Image Quality Assessment in the Spatial Domain" 22 | submitted 23 | 24 | IN NO EVENT SHALL THE UNIVERSITY OF TEXAS AT AUSTIN BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, 25 | OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THIS DATABASE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF TEXAS 26 | AT AUSTIN HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 27 | 28 | THE UNIVERSITY OF TEXAS AT AUSTIN SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 29 | WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, 30 | AND THE UNIVERSITY OF TEXAS AT AUSTIN HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. 31 | 32 | -----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------% 33 | 34 | Author : Anish Mittal 35 | Version : 1.0 36 | 37 | The authors are with the Laboratory for Image and Video Engineering 38 | (LIVE), Department of Electrical and Computer Engineering, The 39 | University of Texas at Austin, Austin, TX. 40 | 41 | Kindly report any suggestions or corrections to mittal.anish@gmail.com 42 | 43 | ======================================================================== 44 | 45 | This is a demonstration of the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) index. 46 | The algorithm is described in: 47 | 48 | A. Mittal, A. K. Moorthy and A. C. Bovik, "No Reference Image Quality Assessment in the Spatial Domain" 49 | 50 | 51 | You can change this program as you like and use it anywhere, but please 52 | refer to its original source (cite our paper and our web page at 53 | http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip). 54 | 55 | ======================================================================== 56 | 57 | Running on Matlab 58 | 59 | 60 | Input : A test image loaded in an array 61 | 62 | Output: A quality score of the image. The score typically has a value 63 | between 0 and 100 (0 represents the best quality, 100 the worst). 64 | 65 | Usage: 66 | 67 | 1. Load the image, for example 68 | 69 | image = imread('testimage1.bmp'); 70 | 71 | 2. Call this function to calculate the quality score: 72 | 73 | qualityscore = brisquescore(image) 74 | 75 | Dependencies: 76 | 77 | Binaries: svm-predict, svm-scale (from LibSVM) - provided with release 78 | MATLAB files: brisquescore.m, estimateggdparam.m, estimateaggdparam.m, brisque_feature.m (provided with release) 79 | 80 | Data files: range_all, model_all (provided with release) 81 | 82 | Image Files: testimage1.bmp, testimage2.bmp 83 | 84 | NOTE: Please rename the .exe1 files to .exe files -- this code will work only on WINDOWS systems. 85 | 86 | ======================================================================== 87 | 88 | Note on training: 89 | This release version of BRISQUE was trained on the entire LIVE database. 90 | 91 | 92 | This program uses LibSVM binaries. 93 | Below is the requried copyright notice for the binaries distributed with this release. 94 | 95 | ==================================================================== 96 | LibSVM tools: svm-predict, svm-scale (binaries) 97 | -------------------------------------------------------------------- 98 | 99 | If you want to run this on a Linux distribution, you could change the lines 100 | 101 | system('svm-scale -r allrange test_ind >> test_ind_scaled'); 102 | system('svm-predict -b 1 test_ind_scaled allmodel output >>dump'); 103 | 104 | to 105 | 106 | system('wine svm-scale -r allrange test_ind >> test_ind_scaled'); 107 | system('wine svm-predict -b 1 test_ind_scaled allmodel output >>dump'); 108 | 109 | Ofcourse, this now requires "wine" to be installed. 110 | 111 | Copyright (c) 2000-2009 Chih-Chung Chang and Chih-Jen Lin 112 | All rights reserved. 113 | 114 | Redistribution and use in source and binary forms, with or without 115 | modification, are permitted provided that the following conditions 116 | are met: 117 | 118 | 1. Redistributions of source code must retain the above copyright 119 | notice, this list of conditions and the following disclaimer. 120 | 121 | 2. Redistributions in binary form must reproduce the above copyright 122 | notice, this list of conditions and the following disclaimer in the 123 | documentation and/or other materials provided with the distribution. 124 | 125 | 3. Neither name of copyright holders nor the names of its contributors 126 | may be used to endorse or promote products derived from this software 127 | without specific prior written permission. 128 | 129 | 130 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 131 | ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 132 | LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 133 | A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR 134 | CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 135 | EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 136 | PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 137 | PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 138 | LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 139 | NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 140 | SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 141 | ==================================================================== -------------------------------------------------------------------------------- /+brisque/svm-predict.exe1: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dsoellinger/blind_image_quality_toolbox/4d12c43c77bba538f684df0b62621e9350854c43/+brisque/svm-predict.exe1 -------------------------------------------------------------------------------- /+brisque/svm-scale.exe1: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dsoellinger/blind_image_quality_toolbox/4d12c43c77bba538f684df0b62621e9350854c43/+brisque/svm-scale.exe1 -------------------------------------------------------------------------------- /+divine/data_live_trained.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dsoellinger/blind_image_quality_toolbox/4d12c43c77bba538f684df0b62621e9350854c43/+divine/data_live_trained.mat -------------------------------------------------------------------------------- /+divine/divine.m: -------------------------------------------------------------------------------- 1 | function q = divine(im) 2 | % DIIVINE Software release. 3 | % 4 | % 5 | % ======================================================================== 6 | % 7 | % -----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------ 8 | % Copyright (c) 2010 The University of Texas at Austin 9 | % All rights reserved. 10 | % 11 | % Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, 12 | % modify, and distribute this code (the source files) and its documentation for 13 | % any purpose, provided that the copyright notice in its entirety appear in all copies of this code, and the 14 | % original source of this code, Laboratory for Image and Video Engineering (LIVE, http://live.ece.utexas.edu) 15 | % and Center for Perceptual Systems (CPS, http://www.cps.utexas.edu) at the University of Texas at Austin (UT Austin, 16 | % http://www.utexas.edu), is acknowledged in any publication that reports research using this code. The research 17 | % is to be cited in the bibliography as: 18 | % 19 | % 1. A. K. Moorthy and A. C. Bovik, "Blind Image Quality Assessment: From Natural 20 | % Scene Statistics to Perceptual Quality", IEEE Transactions on Image Processing, to appear (2011). 21 | % 22 | % 2. A. K. Moorthy and A. C. Bovik, "DIVINE Software Release", 23 | % URL: http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip, 2010 24 | % 25 | % IN NO EVENT SHALL THE UNIVERSITY OF TEXAS AT AUSTIN BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, 26 | % OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THIS DATABASE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF TEXAS 27 | % AT AUSTIN HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 28 | % 29 | % THE UNIVERSITY OF TEXAS AT AUSTIN SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 30 | % WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, 31 | % AND THE UNIVERSITY OF TEXAS AT AUSTIN HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. 32 | % 33 | % -----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------% 34 | % 35 | % Author : Anush Krishna Moorthy 36 | % Version : 1.1 37 | % 38 | % The authors are with the Laboratory for Image and Video Engineering 39 | % (LIVE), Department of Electrical and Computer Engineering, The 40 | % University of Texas at Austin, Austin, TX. 41 | % 42 | % Kindly report any suggestions or corrections to anushmoorthy@gmail.com 43 | % 44 | % ======================================================================== 45 | % 46 | % This is a demonstration of the Distortion Identification based image Verity and INtegrity Evaluation (DIVINE) index. 47 | % It is an implementation of the BIQI in the reference. 48 | % The algorithm is described in: 49 | % A. K. Moorthy and A. C. Bovik, "Blind Image Quality Assessment: From Natural 50 | % Scene Statistics to Perceptual Quality", IEEE Transactions on Image Processing, to appear (2011). 51 | % 52 | % You can change this program as you like and use it anywhere, but please 53 | % refer to its original source (cite our paper and our web page at 54 | % http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip). 55 | % 56 | % Input : A test 8bits/pixel grayscale image loaded in a 2-D array 57 | % Output: A quality score of the image. The score typically has a value 58 | % between 0 and 100 (0 represents the best quality, 100 the worst). 59 | % 60 | % Usage: 61 | % 62 | % 1. Load the image, for example 63 | % 64 | % image = rgb2gray(imread('testimage.jpg')); 65 | % 66 | % 2. Call this function to calculate the quality score: 67 | % 68 | % quality = divine(image) 69 | % 70 | % Dependencies: 71 | % Steerable Pyramid Toolbox, Download from: http://www.cns.nyu.edu/~eero/steerpyr/ 72 | % LibSVM package for MATLAB, Download from: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ 73 | % You may need the MATLAB Image Processing Toolbox 74 | % 75 | % Dependencies-- 76 | % 77 | % MATLAB files: ssim_index_new.m, norm_sender_normalized.m, find_spatial_hist_fast.m, divine_overall_quality.m 78 | % divine_feature_extract.m, map_matrix_to_closest_vec.m (provided with release) 79 | % 80 | % Data files: data_live_trained.mat (provided with release) 81 | % 82 | % This code has been tested on Windows and Mac OSX (Snow Leopard) 83 | % 84 | % ========================================================================% 85 | % Note on training: 86 | % This release version of BIQI was trained on the entire LIVE database. 87 | % 88 | % 89 | 90 | import divine.* 91 | 92 | 93 | 94 | f = divine_feature_extract(im); 95 | q = divine_overall_quality(f); -------------------------------------------------------------------------------- /+divine/divine_feature_extract.m: -------------------------------------------------------------------------------- 1 | function f = divine_feature_extract(im) 2 | import divine.* 3 | 4 | % Function to extract features given an image 5 | %% Constants 6 | num_or = 6; 7 | num_scales = 2; 8 | gam = 0.2:0.001:10; 9 | r_gam = gamma(1./gam).*gamma(3./gam)./(gamma(2./gam)).^2; 10 | 11 | %% Wavelet Transform + Div. Norm. 12 | 13 | if(size(im,3)~=1) 14 | im = (double(rgb2gray(im))); 15 | end 16 | [pyr pind] = buildSFpyr(im,num_scales,num_or-1); 17 | 18 | [subband size_band] = norm_sender_normalized(pyr,pind,num_scales,num_or,1,1,3,3,50); 19 | 20 | 21 | 22 | f = []; 23 | 24 | %% Marginal Statistics 25 | 26 | 27 | h_horz_curr = []; 28 | for ii = 1:length(subband) 29 | t = subband{ii}; 30 | mu_horz = mean(t); 31 | sigma_sq_horz(ii) = mean((t-mu_horz).^2); 32 | E_horz = mean(abs(t-mu_horz)); 33 | rho_horz = sigma_sq_horz(ii)/E_horz^2; 34 | [min_difference, array_position] = min(abs(rho_horz - r_gam)); 35 | gam_horz(ii) = gam(array_position); 36 | end 37 | f = [f sigma_sq_horz gam_horz]; % ind. subband stats f1-f24 38 | 39 | %% Joint Statistics 40 | 41 | 42 | clear sigma_sq_horz gam_horz 43 | for ii = 1:length(subband)/2 44 | t = [subband{ii}; subband{ii+num_or}]; 45 | mu_horz = mean(t); 46 | sigma_sq_horz(ii) = mean((t-mu_horz).^2); 47 | E_horz = mean(abs(t-mu_horz)); 48 | rho_horz = sigma_sq_horz(ii)/E_horz^2; 49 | [min_difference, array_position] = min(abs(rho_horz - r_gam)); 50 | gam_horz(ii) = gam(array_position); 51 | end 52 | f = [f gam_horz]; 53 | t = cell2mat(subband'); 54 | mu = mean(t); 55 | sigma_sq = mean((t-mu_horz).^2); 56 | E_horz = mean(abs(t-mu_horz)); 57 | rho_horz = sigma_sq/E_horz^2; 58 | [min_difference, array_position] = min(abs(rho_horz - r_gam)); 59 | gam_horz = gam(array_position); 60 | 61 | f = [f gam_horz]; 62 | 63 | %% Hp-BP correlations 64 | 65 | 66 | hp_band = pyrBand(pyr,pind,1); 67 | for ii = 1:length(subband) 68 | curr_band = pyrBand(pyr,pind,ii+1); 69 | [ssim_val(ii), ssim_map, cs_val(ii)] = ssim_index_new(imresize(curr_band,size(hp_band)),hp_band); 70 | end 71 | f = [f cs_val]; 72 | 73 | 74 | %% Spatial Correlation 75 | 76 | b = []; 77 | for i = 1:length(subband)/2 78 | b = [b find_spatial_hist_fast(reshape(subband{i},(size_band(i,:))))]; 79 | end 80 | f = [f b]; 81 | 82 | %% Orientation feature... 83 | 84 | l = 1; clear ssim_val cs_val 85 | for i = 1:length(subband)/2 86 | for j = i+1:length(subband)/2 87 | [ssim_val(l), ssim_map, cs_val(l)] = ssim_index_new(reshape(subband{i},size_band(i,:)),reshape(subband{j},size_band(j,:))); 88 | l = l + 1; 89 | end 90 | end 91 | f = [f cs_val]; 92 | 93 | 94 | -------------------------------------------------------------------------------- /+divine/divine_overall_quality.m: -------------------------------------------------------------------------------- 1 | function q = divine_overall_quality(r) 2 | 3 | fullpath = mfilename('fullpath'); 4 | idx = find(fullpath == filesep); 5 | dfolder = fullpath(1:(idx(end)-1)); 6 | 7 | % Function to compute overall quality given feature vector 'r' 8 | 9 | load(fullfile(dfolder,'data_live_trained.mat')) 10 | 11 | %% Classification 12 | atrain = repmat(a_class,[size(r,1) 1]);btrain = repmat(b_class,[size(r,1) 1]); 13 | x_curr = atrain.*r+btrain; 14 | 15 | % [pred_class acc p] = svmpredict(1,x_curr,model_class,'-b 1'); 16 | % use `git clone git://github.com/gregfreeman/libsvm.git -b new_matlab_interface` 17 | 18 | [pred_class p] = svmpredict(x_curr,model_class,struct('output','probability')); 19 | 20 | 21 | %% Regression 22 | for i = 1:5 23 | atrain = repmat(a_reg(i,:),[size(r,1) 1]);btrain = repmat(b_reg(i,:),[size(r,1) 1]); 24 | x_curr = atrain.*r+btrain; 25 | % [q(i) reg_acc(i,:)] = svmpredict(1,x_curr,model_reg{i}); 26 | [q(i)] = svmpredict(x_curr,model_reg{i}); 27 | end 28 | %% Final Score 29 | % clc 30 | q = sum(p.*q); 31 | -------------------------------------------------------------------------------- /+divine/find_spatial_hist_fast.m: -------------------------------------------------------------------------------- 1 | function [b rho ypred matrix xval] = find_spatial_hist_fast(x) 2 | % Function to find the spatial histogram for IMAGE and then compute Corr 3 | % and fit 3-parameter logistic. returns logistic fitting value. 4 | import divine.* 5 | 6 | [nrows ncolms] = size(x); 7 | im = x; 8 | nbins = 100; 9 | max_x = 25; 10 | ss_fact = 4; 11 | [hval bins] = hist(x(:),nbins); 12 | x = map_matrix_to_closest_vec(im,bins); 13 | for j = 1:ss_fact:max_x 14 | matrix = zeros(nbins); 15 | 16 | for i = 1:nbins 17 | 18 | [r c] = find(x==bins(i)); 19 | 20 | if(isempty(r)~=1) 21 | h = zeros(1,nbins); 22 | 23 | ctemp = c-j; 24 | rtemp = r;rtemp(ctemp<1) = []; 25 | ctemp(ctemp<1) = []; 26 | ind = sub2ind(size(im),rtemp,ctemp); 27 | if(~isempty(ind)) 28 | h = h+hist(im(ind),bins); 29 | end 30 | 31 | ctemp = c+j; rtemp = r;rtemp(ctemp>ncolms) = []; 32 | ctemp(ctemp>ncolms) = []; 33 | ind = sub2ind(size(im),rtemp,ctemp); 34 | if(~isempty(ind)) 35 | h = h+hist(im(ind),bins); 36 | end 37 | 38 | rtemp = r-j;ctemp = c;ctemp(rtemp<1) = []; 39 | rtemp(rtemp<1) = []; 40 | ind = sub2ind(size(im),rtemp,ctemp); 41 | if(~isempty(ind)) 42 | h = h+hist(im(ind),bins); 43 | end 44 | 45 | rtemp = r+j; 46 | ctemp = c;ctemp(rtemp>nrows) = []; 47 | rtemp(rtemp>nrows) = []; 48 | ind = sub2ind(size(im),rtemp,ctemp); 49 | if(~isempty(ind)) 50 | h = h+hist(im(ind),bins); 51 | end 52 | 53 | rtemp = r+j;ctemp = c;ctemp(rtemp>nrows) = [];rtemp(rtemp>nrows) = []; 54 | ctemp = ctemp+j;rtemp(ctemp>ncolms) = []; ctemp(ctemp>ncolms) = []; 55 | ind = sub2ind(size(im),rtemp,ctemp); 56 | if(~isempty(ind)) 57 | h = h+hist(im(ind),bins); 58 | end 59 | 60 | rtemp = r+j;ctemp = c;ctemp(rtemp>nrows) = [];rtemp(rtemp>nrows) = []; 61 | ctemp = ctemp-j; rtemp(ctemp<1) = [];ctemp(ctemp<1) = []; 62 | ind = sub2ind(size(im),rtemp,ctemp); 63 | if(~isempty(ind)) 64 | h = h+hist(im(ind),bins); 65 | end 66 | 67 | rtemp = r-j;ctemp = c;ctemp(rtemp<1) = [];rtemp(rtemp<1) = []; 68 | ctemp = ctemp+j;rtemp(ctemp>ncolms) = []; ctemp(ctemp>ncolms) = []; 69 | ind = sub2ind(size(im),rtemp,ctemp); 70 | if(~isempty(ind)) 71 | h = h+hist(im(ind),bins); 72 | end 73 | 74 | rtemp = r-j;ctemp = c; ctemp(rtemp<1) = []; rtemp(rtemp<1) = []; 75 | ctemp = ctemp-j;rtemp(ctemp<1) = []; ctemp(ctemp<1) = []; 76 | ind = sub2ind(size(im),rtemp,ctemp); 77 | if(~isempty(ind)) 78 | h = h+hist(im(ind),bins); 79 | end 80 | 81 | matrix(i,:) = h; 82 | end 83 | 84 | end 85 | X = [bins]'; Y = X; matrix = matrix/sum(sum(matrix)); 86 | px = sum(matrix,2); py = sum(matrix,1)'; 87 | mu_x = sum(X.*px); mu_y = sum(Y.*py); 88 | sigma2_x = sum((X-mu_x).^2.*px); sigma2_y = sum((Y-mu_x).^2.*py); 89 | [xx yy] = meshgrid(X,Y); 90 | rho(j) = (sum(sum(xx.*yy.*matrix))-mu_x.*mu_y)/(sqrt(sigma2_x)*sqrt(sigma2_y)); 91 | 92 | end 93 | 94 | rho = [rho(1:ss_fact:end)]; 95 | xval = [1:ss_fact:max_x]; 96 | % b = nlinfit([0 1:4:100],rho,@fit_spatial_corr,ones(1,5)); 97 | pp = polyfit([1:ss_fact:max_x],rho,3); 98 | % ypred = fit_spatial_corr(b,1:4:100); 99 | ypred = polyval(pp,[1:ss_fact:max_x]); 100 | err = sum((ypred-rho).^2); 101 | b = [pp err]; 102 | % 103 | % figure 104 | % plot(rho,'r','LineWidth',3); hold on 105 | % plot(ypred,'k'); title(['Error:',num2str( sum((ypred-rho).^2))]); 106 | % toc 107 | % subplot(1,3,1) 108 | % imagesc(log(matrix+1));colormap(gray);axis xy 109 | % subplot(1,3,2) 110 | % bar(bins,sum(matrix,1)); axis([1 255 0 max(sum(matrix,1))]) 111 | % subplot(1,3,3) 112 | % bar(bins,sum(matrix,2));axis([1 255 0 max(sum(matrix,2))]) 113 | -------------------------------------------------------------------------------- /+divine/map_matrix_to_closest_vec.m: -------------------------------------------------------------------------------- 1 | function x = map_matrix_to_closest_vec(matrix,vec) 2 | % Function to map the values in the matrix to a value from the vector so 3 | % that the values in x are the same as those in vec 4 | % vec is a 1xm vector and x is a pxq matrix 5 | [nrows ncolms ] = size(matrix); 6 | x = zeros(size(matrix)); 7 | if(nrows<=ncolms) 8 | for i = 1:nrows 9 | curr = matrix(i,:)'; 10 | [minval ind] = min(abs(repmat(curr,[1,length(vec)])-repmat(vec,([ncolms 1]))),[],2); 11 | x(i,:) = vec(ind); 12 | end 13 | elseif(nrows>ncolms) 14 | for i = 1:ncolms 15 | curr = matrix(:,i); 16 | [minval ind] = min(abs(repmat(curr,[1,length(vec)])-repmat(vec,([nrows 1]))),[],2); 17 | x(:,i) = vec(ind); 18 | end 19 | end -------------------------------------------------------------------------------- /+divine/norm_sender_normalized.m: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | % try to fix the range of z field 5 | function [subband size_band] = norm_sender_normalized(pyro,pind,Nsc,Nor,parent,neighbor,blSzX,blSzY,nbins) 6 | guardband = 16; 7 | pyro = real(pyro); 8 | Nband = size(pind,1)-1; 9 | band = [1 3 6 8 9 11]; 10 | zRange = 15; 11 | 12 | p = 1; 13 | for scale=1:Nsc 14 | for orien=1:Nor 15 | nband = (scale-1)*Nor+orien+1; % except the ll 16 | % if(prod(band-nband-1) ~=0) 17 | % continue; 18 | % end 19 | aux = pyrBand(pyro, pind, nband); 20 | [Nsy,Nsx] = size(aux); 21 | 22 | prnt = parent & (nband < Nband-Nor); % has the subband a parent? 23 | BL = zeros(size(aux,1),size(aux,2),1 + prnt); 24 | BL(:,:,1) = aux; 25 | if prnt, 26 | auxp = pyrBand(pyro, pind, nband+Nor); 27 | % if nband>Nor+1, % resample 2x2 the parent if not in the high-pass oriented subbands. 28 | % auxp = real(imenlarge2(auxp)); % this was uncommented 29 | auxp = real(imresize(auxp,2)); % 30 | % end 31 | % fprintf('parent band and size is %d %d %d \n',nband+Nor,Nsy,Nsx) 32 | BL(:,:,2) = auxp(1:Nsy,1:Nsx); 33 | end 34 | y=BL; 35 | [nv,nh,nb] = size(y); 36 | block = [blSzX blSzY]; 37 | 38 | nblv = nv-block(1)+1; % Discard the outer coefficients 39 | nblh = nh-block(2)+1; % for the reference (centrral) coefficients (to avoid boundary effects) 40 | nexp = nblv*nblh; % number of coefficients considered 41 | N = prod(block) + prnt; % size of the neighborhood 42 | 43 | Ly = (block(1)-1)/2; % block(1) and block(2) must be odd! 44 | Lx = (block(2)-1)/2; 45 | if (Ly~=floor(Ly))|(Lx~=floor(Lx)), 46 | error('Spatial dimensions of neighborhood must be odd!'); 47 | end 48 | Y = zeros(nexp,N); % It will be the observed signal (rearranged in nexp neighborhoods) 49 | % Rearrange observed samples in 'nexp' neighborhoods 50 | n = 0; 51 | for ny=-Ly:Ly, % spatial neighbors 52 | for nx=-Lx:Lx, 53 | n = n + 1; 54 | foo = shift(y(:,:,1),[ny nx]); 55 | foo = foo(Ly+1:Ly+nblv,Lx+1:Lx+nblh); 56 | Y(:,n) = (foo(:)); 57 | end 58 | end 59 | 60 | if prnt, % parent 61 | n = n + 1; 62 | foo = y(:,:,2); 63 | foo = foo(Ly+1:Ly+nblv,Lx+1:Lx+nblh); 64 | Y(:,n) = (foo(:)); 65 | end 66 | 67 | % including neighbor 68 | if neighbor, 69 | for neib=1:Nor 70 | if neib == orien 71 | continue; 72 | end 73 | n=n+1; 74 | nband1 = (scale-1)*Nor+neib+1; % except the ll 75 | aux1 = pyrBand(pyro, pind, nband1); 76 | aux1 = aux1(Ly+1:Ly+nblv,Lx+1:Lx+nblh); 77 | Y(:,n) = (aux1(:)); 78 | end 79 | end 80 | 81 | C_x = innerProd(Y)/nexp; 82 | % C_x is positive definete covariance matrix 83 | [Q,L] = eig(C_x); 84 | % correct possible negative eigenvalues, without changing the overall variance 85 | L = diag(diag(L).*(diag(L)>0))*sum(diag(L))/(sum(diag(L).*(diag(L)>0))+(sum(diag(L).*(diag(L)>0))==0)); 86 | C_x = Q*L*Q'; 87 | 88 | o_c = aux(Ly+1:Ly+nblv,Lx+1:Lx+nblh); 89 | o_c = (o_c(:)); 90 | o_c = o_c - mean(o_c); 91 | [hy rangeo] = hist(o_c,nbins); 92 | hy=hy/sum(hy); 93 | 94 | tempY = (Y*pinv(C_x)).*Y/N; 95 | z = sqrt(sum(tempY,2)); 96 | ind = find(z~=0); 97 | 98 | g_c =o_c(ind)./z(ind); 99 | 100 | 101 | % consider the guardband 102 | 103 | g_c=reshape(g_c,nblv,nblh); 104 | gb = guardband/(2^(scale-1)); 105 | g_c=g_c(gb+1:end-gb, gb+1:end-gb); 106 | size_band(p,:) = size(g_c); 107 | g_c= (g_c(:)); 108 | g_c = g_c - mean(g_c); 109 | 110 | subband{p} = g_c; 111 | 112 | % band_var(p) = var(g_c); 113 | p = p+1; 114 | 115 | % maxz = zRange*4^(scale-1); 116 | % 117 | % range = -maxz:2*maxz/(nbins-1):maxz; 118 | % 119 | % [hyg rangeg] = hist(g_c,range); 120 | % hyg=hyg/sum(hyg); 121 | % 122 | % mu=mean(g_c); 123 | % o_sigma(nband-1)=std(g_c); 124 | % o_range(nband-1,:)= rangeg; 125 | % 126 | % sigma = std(g_c); 127 | % hm = 1/sqrt(2*pi)/sigma*exp(-(range-mu).^2/2/(sigma^2)); 128 | % hm = hm/sum(hm); 129 | % distance(nband-1) = kld(hm,hyg); 130 | end 131 | end 132 | return; 133 | 134 | -------------------------------------------------------------------------------- /+divine/readme.txt: -------------------------------------------------------------------------------- 1 | DIIVINE Software release. 2 | 3 | 4 | ======================================================================== 5 | 6 | -----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------ 7 | Copyright (c) 2010 The University of Texas at Austin 8 | All rights reserved. 9 | 10 | Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, 11 | modify, and distribute this code (the source files) and its documentation for 12 | any purpose, provided that the copyright notice in its entirety appear in all copies of this code, and the 13 | original source of this code, Laboratory for Image and Video Engineering (LIVE, http://live.ece.utexas.edu) 14 | and Center for Perceptual Systems (CPS, http://www.cps.utexas.edu) at the University of Texas at Austin (UT Austin, 15 | http://www.utexas.edu), is acknowledged in any publication that reports research using this code. The research 16 | is to be cited in the bibliography as: 17 | 18 | 1. A. K. Moorthy and A. C. Bovik, "Blind Image Quality Assessment: From Natural 19 | Scene Statistics to Perceptual Quality", IEEE Transactions on Image Processing, to appear (2011). 20 | 21 | 2. A. K. Moorthy and A. C. Bovik, "DIVINE Software Release", 22 | URL: http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip, 2010 23 | 24 | IN NO EVENT SHALL THE UNIVERSITY OF TEXAS AT AUSTIN BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, 25 | OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THIS DATABASE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF TEXAS 26 | AT AUSTIN HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 27 | 28 | THE UNIVERSITY OF TEXAS AT AUSTIN SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 29 | WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, 30 | AND THE UNIVERSITY OF TEXAS AT AUSTIN HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. 31 | 32 | -----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------% 33 | 34 | Author : Anush Krishna Moorthy 35 | Version : 1.1 36 | 37 | The authors are with the Laboratory for Image and Video Engineering 38 | (LIVE), Department of Electrical and Computer Engineering, The 39 | University of Texas at Austin, Austin, TX. 40 | 41 | Kindly report any suggestions or corrections to anushmoorthy@gmail.com 42 | 43 | ======================================================================== 44 | 45 | This is a demonstration of the Distortion Identification based image Verity and INtegrity Evaluation (DIVINE) index. 46 | It is an implementation of the BIQI in the reference. 47 | The algorithm is described in: 48 | A. K. Moorthy and A. C. Bovik, "Blind Image Quality Assessment: From Natural 49 | Scene Statistics to Perceptual Quality", IEEE Transactions on Image Processing, to appear (2011). 50 | 51 | You can change this program as you like and use it anywhere, but please 52 | refer to its original source (cite our paper and our web page at 53 | http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip). 54 | 55 | Input : A test 8bits/pixel grayscale image loaded in a 2-D array 56 | Output: A quality score of the image. The score typically has a value 57 | between 0 and 100 (0 represents the best quality, 100 the worst). 58 | 59 | Usage: 60 | 61 | 1. Load the image, for example 62 | 63 | image = rgb2gray(imread('testimage.jpg')); 64 | 65 | 2. Call this function to calculate the quality score: 66 | 67 | quality = divine(image) 68 | 69 | Dependencies: 70 | Steerable Pyramid Toolbox, Download from: http://www.cns.nyu.edu/~eero/steerpyr/ 71 | LibSVM package for MATLAB, Download from: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ 72 | You may need the MATLAB Image Processing Toolbox 73 | 74 | Dependencies-- 75 | 76 | MATLAB files: ssim_index_new.m, norm_sender_normalized.m, find_spatial_hist_fast.m, divine_overall_quality.m 77 | divine_feature_extract.m, map_matrix_to_closest_vec.m (provided with release) 78 | 79 | Data files: data_live_trained.mat (provided with release) 80 | 81 | This code has been tested on Windows and Mac OSX (Snow Leopard) 82 | 83 | ======================================================================== 84 | 85 | Note on training: 86 | This release version of BIQI was trained on the entire LIVE database. 87 | -------------------------------------------------------------------------------- /+divine/ssim_index_new.m: -------------------------------------------------------------------------------- 1 | function [mssim, ssim_map, mcs, cs_map,sigma1_sq,mu1] = ssim_index_new(img1, img2, K, wind) 2 | 3 | if (nargin < 2 | nargin > 4) 4 | ssim_index = -Inf; 5 | ssim_map = -Inf; 6 | return; 7 | end 8 | 9 | if (size(img1) ~= size(img2)) 10 | ssim_index = -Inf; 11 | ssim_map = -Inf; 12 | return; 13 | end 14 | 15 | [M N] = size(img1); 16 | 17 | if (nargin == 2) 18 | if ((M < 11) | (N < 11)) 19 | ssim_index = -Inf; 20 | ssim_map = -Inf; 21 | return 22 | end 23 | wind = fspecial('gaussian', 11, 1.5); % 24 | K(1) = 0.01; % default settings 25 | K(2) = 0.03; % 26 | end 27 | 28 | if (nargin == 3) 29 | if ((M < 11) | (N < 11)) 30 | ssim_index = -Inf; 31 | ssim_map = -Inf; 32 | return 33 | end 34 | wind = fspecial('gaussian', 11, 1.5); 35 | if (length(K) == 2) 36 | if (K(1) < 0 | K(2) < 0) 37 | ssim_index = -Inf; 38 | ssim_map = -Inf; 39 | return; 40 | end 41 | else 42 | ssim_index = -Inf; 43 | ssim_map = -Inf; 44 | return; 45 | end 46 | end 47 | 48 | if (nargin == 4) 49 | [H W] = size(wind); 50 | if ((H*W) < 4 | (H > M) | (W > N)) 51 | ssim_index = -Inf; 52 | ssim_map = -Inf; 53 | return 54 | end 55 | if (length(K) == 2) 56 | if (K(1) < 0 | K(2) < 0) 57 | ssim_index = -Inf; 58 | ssim_map = -Inf; 59 | return; 60 | end 61 | else 62 | ssim_index = -Inf; 63 | ssim_map = -Inf; 64 | return; 65 | end 66 | end 67 | 68 | C1 = (K(1)*255)^2; 69 | C2 = (K(2)*255)^2; 70 | wind = wind/sum(sum(wind)); 71 | % all 'same' s were 'valid' --anush 72 | mu1 = filter2(wind, img1, 'valid'); 73 | mu2 = filter2(wind, img2, 'valid'); 74 | mu1_sq = mu1.*mu1; 75 | mu2_sq = mu2.*mu2; 76 | mu1_mu2 = mu1.*mu2; 77 | sigma1_sq = filter2(wind, img1.*img1, 'valid') - mu1_sq; 78 | sigma2_sq = filter2(wind, img2.*img2, 'valid') - mu2_sq; 79 | sigma12 = filter2(wind, img1.*img2, 'valid') - mu1_mu2; 80 | 81 | if (C1 > 0 & C2 > 0) 82 | ssim_map = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))./((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2)); 83 | cs_map = (2*sigma12 + C2)./(sigma1_sq + sigma2_sq + C2); 84 | else 85 | numerator1 = 2*mu1_mu2 + C1; 86 | numerator2 = 2*sigma12 + C2; 87 | denominator1 = mu1_sq + mu2_sq + C1; 88 | denominator2 = sigma1_sq + sigma2_sq + C2; 89 | 90 | ssim_map = ones(size(mu1)); 91 | index = (denominator1.*denominator2 > 0); 92 | ssim_map(index) = (numerator1(index).*numerator2(index))./(denominator1(index).*denominator2(index)); 93 | index = (denominator1 ~= 0) & (denominator2 == 0); 94 | ssim_map(index) = numerator1(index)./denominator1(index); 95 | 96 | cs_map = ones(size(mu1)); 97 | index = denominator2 > 0; 98 | cs_map(index) = numerator2(index)./denominator2(index); 99 | end 100 | 101 | mssim = mean2(ssim_map); 102 | mcs = mean2(cs_map); 103 | 104 | return -------------------------------------------------------------------------------- /+iqvg/IQVG.m: -------------------------------------------------------------------------------- 1 | function [ score ] = IQVG(img) 2 | 3 | import iqvg.*; 4 | 5 | patchSize = 7; 6 | maxFrequency = 1; 7 | frequencyNum = 5; 8 | orientionsNum = 4; 9 | patchNum = 5000; %more patches, more stable the result is 10 | gfFeature = sample_img(img, patchSize, maxFrequency, frequencyNum, orientionsNum, patchNum); 11 | % 200 is the number of bins that we use to encode the Gabor feature. 12 | imgPre = buildHistogram (gfFeature, patchNum, 200); 13 | outSVMData(imgPre); 14 | % conducted this in the cmd console 15 | %svm-scale.exe -r scale_parameter f.txt > f_scaled.txt 16 | system('/usr/local/Cellar/libsvm/3.22/bin/svm-scale -r +iqvg/scale_parameter f.txt > f_scaled.txt'); 17 | [~, f_scaled] = libsvmread('f_scaled.txt'); 18 | load('+iqvg/model.mat'); 19 | [predict_label, accuracy, dec_values] = svmpredict(-1, f_scaled, model, '-b 0'); 20 | score = predict_label; 21 | 22 | delete 'f.txt'; 23 | delete 'f_scaled.txt'; 24 | end 25 | 26 | -------------------------------------------------------------------------------- /+iqvg/SDSP.m: -------------------------------------------------------------------------------- 1 | function VSMap = SDSP(image) 2 | % ======================================================================== 3 | % SDSP algorithm for salient region detection from a given image. 4 | % Copyright(c) 2013 Lin ZHANG, School of Software Engineering, Tongji 5 | % University 6 | % All Rights Reserved. 7 | % ---------------------------------------------------------------------- 8 | % Permission to use, copy, or modify this software and its documentation 9 | % for educational and research purposes only and without fee is here 10 | % granted, provided that this copyright notice and the original authors' 11 | % names appear on all copies and supporting documentation. This program 12 | % shall not be used, rewritten, or adapted as the basis of a commercial 13 | % software or hardware product without first obtaining permission of the 14 | % authors. The authors make no representations about the suitability of 15 | % this software for any purpose. It is provided "as is" without express 16 | % or implied warranty. 17 | %---------------------------------------------------------------------- 18 | % 19 | % This is an implementation of the algorithm for calculating the 20 | % SDSP (Saliency Detection by combining Simple Priors). 21 | % 22 | % Please refer to the following paper 23 | % 24 | % Lin Zhang, Zhongyi Gu, and Hongyu Li,"SDSP: a novel saliency detection 25 | % method by combining simple priors", ICIP, 2013. 26 | % 27 | %---------------------------------------------------------------------- 28 | % 29 | %Input : image: an uint8 RGB image with dynamic range [0, 255] for each 30 | %channel 31 | % 32 | %Output: VSMap: the visual saliency map extracted by the SDSP algorithm. 33 | %Data range for VSMap is [0, 255]. So, it can be regarded as a common 34 | %gray-scale image. 35 | % 36 | %----------------------------------------------------------------------- 37 | 38 | sigmaF = 6.2; 39 | omega0 = 0.002; 40 | sigmaD = 114; 41 | sigmaC = 0.25; 42 | %convert the image into LAB color space 43 | [oriRows, oriCols, junk] = size(image); 44 | image = double(image); 45 | dsImage(:,:,1) = imresize(image(:,:,1), [256, 256],'bilinear'); 46 | dsImage(:,:,2) = imresize(image(:,:,2), [256, 256],'bilinear'); 47 | dsImage(:,:,3) = imresize(image(:,:,3), [256, 256],'bilinear'); 48 | lab = RGB2Lab(dsImage); 49 | 50 | LChannel = lab(:,:,1); 51 | AChannel = lab(:,:,2); 52 | BChannel = lab(:,:,3); 53 | 54 | LFFT = fft2(double(LChannel)); 55 | AFFT = fft2(double(AChannel)); 56 | BFFT = fft2(double(BChannel)); 57 | 58 | [rows, cols, junk] = size(dsImage); 59 | LG = logGabor(rows,cols,omega0,sigmaF); 60 | FinalLResult = real(ifft2(LFFT.*LG)); 61 | FinalAResult = real(ifft2(AFFT.*LG)); 62 | FinalBResult = real(ifft2(BFFT.*LG)); 63 | 64 | SFMap = sqrt(FinalLResult.^2 + FinalAResult.^2 + FinalBResult.^2); 65 | 66 | %the central areas will have a bias towards attention 67 | coordinateMtx = zeros(rows, cols, 2); 68 | coordinateMtx(:,:,1) = repmat((1:1:rows)', 1, cols); 69 | coordinateMtx(:,:,2) = repmat(1:1:cols, rows, 1); 70 | 71 | centerY = rows / 2; 72 | centerX = cols / 2; 73 | centerMtx(:,:,1) = ones(rows, cols) * centerY; 74 | centerMtx(:,:,2) = ones(rows, cols) * centerX; 75 | SDMap = exp(-sum((coordinateMtx - centerMtx).^2,3) / sigmaD^2); 76 | 77 | %warm colors have a bias towards attention 78 | maxA = max(AChannel(:)); 79 | minA = min(AChannel(:)); 80 | normalizedA = (AChannel - minA) / (maxA - minA); 81 | 82 | maxB = max(BChannel(:)); 83 | minB = min(BChannel(:)); 84 | normalizedB = (BChannel - minB) / (maxB - minB); 85 | 86 | labDistSquare = normalizedA.^2 + normalizedB.^2; 87 | SCMap = 1 - exp(-labDistSquare / (sigmaC^2)); 88 | VSMap = SFMap .* SDMap .* SCMap; 89 | 90 | VSMap = imresize(VSMap, [oriRows, oriCols],'bilinear'); 91 | VSMap = uint8(mat2gray(VSMap) * 255); 92 | return; 93 | 94 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 95 | function labImage = RGB2Lab(image) 96 | 97 | image = double(image); 98 | normalizedR = image(:,:,1) / 255; 99 | normalizedG = image(:,:,2) / 255; 100 | normalizedB = image(:,:,3) / 255; 101 | 102 | RSmallerOrEqualto4045 = normalizedR <= 0.04045; 103 | RGreaterThan4045 = 1 - RSmallerOrEqualto4045; 104 | tmpR = (normalizedR / 12.92) .* RSmallerOrEqualto4045; 105 | tmpR = tmpR + power((normalizedR + 0.055)/1.055,2.4) .* RGreaterThan4045; 106 | 107 | GSmallerOrEqualto4045 = normalizedG <= 0.04045; 108 | GGreaterThan4045 = 1 - GSmallerOrEqualto4045; 109 | tmpG = (normalizedG / 12.92) .* GSmallerOrEqualto4045; 110 | tmpG = tmpG + power((normalizedG + 0.055)/1.055,2.4) .* GGreaterThan4045; 111 | 112 | BSmallerOrEqualto4045 = normalizedB <= 0.04045; 113 | BGreaterThan4045 = 1 - BSmallerOrEqualto4045; 114 | tmpB = (normalizedB / 12.92) .* BSmallerOrEqualto4045; 115 | tmpB = tmpB + power((normalizedB + 0.055)/1.055,2.4) .* BGreaterThan4045; 116 | 117 | X = tmpR*0.4124564 + tmpG*0.3575761 + tmpB*0.1804375; 118 | Y = tmpR*0.2126729 + tmpG*0.7151522 + tmpB*0.0721750; 119 | Z = tmpR*0.0193339 + tmpG*0.1191920 + tmpB*0.9503041; 120 | 121 | epsilon = 0.008856; %actual CIE standard 122 | kappa = 903.3; %actual CIE standard 123 | 124 | Xr = 0.9642; %reference white D50 125 | Yr = 1.0; %reference white 126 | Zr = 0.8251; %reference white 127 | 128 | xr = X/Xr; 129 | yr = Y/Yr; 130 | zr = Z/Zr; 131 | 132 | xrGreaterThanEpsilon = xr > epsilon; 133 | xrSmallerOrEqualtoEpsilon = 1 - xrGreaterThanEpsilon; 134 | fx = power(xr, 1.0/3.0) .* xrGreaterThanEpsilon; 135 | fx = fx + (kappa*xr + 16.0)/116.0 .* xrSmallerOrEqualtoEpsilon; 136 | 137 | yrGreaterThanEpsilon = yr > epsilon; 138 | yrSmallerOrEqualtoEpsilon = 1 - yrGreaterThanEpsilon; 139 | fy = power(yr, 1.0/3.0) .* yrGreaterThanEpsilon; 140 | fy = fy + (kappa*yr + 16.0)/116.0 .* yrSmallerOrEqualtoEpsilon; 141 | 142 | zrGreaterThanEpsilon = zr > epsilon; 143 | zrSmallerOrEqualtoEpsilon = 1 - zrGreaterThanEpsilon; 144 | fz = power(zr, 1.0/3.0) .* zrGreaterThanEpsilon; 145 | fz = fz + (kappa*zr + 16.0)/116.0 .* zrSmallerOrEqualtoEpsilon; 146 | 147 | [rows,cols,junk] = size(image); 148 | labImage = zeros(rows,cols,3); 149 | labImage(:,:,1) = 116.0 * fy - 16.0; 150 | labImage(:,:,2) = 500.0 * (fx - fy); 151 | labImage(:,:,3) = 200.0 * (fy - fz); 152 | return; 153 | 154 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 155 | function LG = logGabor(rows,cols,omega0,sigmaF) 156 | [u1, u2] = meshgrid(([1:cols]-(fix(cols/2)+1))/(cols-mod(cols,2)), ... 157 | ([1:rows]-(fix(rows/2)+1))/(rows-mod(rows,2))); 158 | mask = ones(rows, cols); 159 | for rowIndex = 1:rows 160 | for colIndex = 1:cols 161 | if u1(rowIndex, colIndex)^2 + u2(rowIndex, colIndex)^2 > 0.25 162 | mask(rowIndex, colIndex) = 0; 163 | end 164 | end 165 | end 166 | u1 = u1 .* mask; 167 | u2 = u2 .* mask; 168 | 169 | u1 = ifftshift(u1); 170 | u2 = ifftshift(u2); 171 | 172 | radius = sqrt(u1.^2 + u2.^2); 173 | radius(1,1) = 1; 174 | 175 | LG = exp((-(log(radius/omega0)).^2) / (2 * (sigmaF^2))); 176 | LG(1,1) = 0; 177 | return; 178 | 179 | 180 | -------------------------------------------------------------------------------- /+iqvg/buildHistogram.m: -------------------------------------------------------------------------------- 1 | function [fv] = buildHistogram (bags, numInstances, p) 2 | 3 | p_p = 1/p; 4 | matrix = eye(p); 5 | 6 | fv = zeros( size(bags, 1)/numInstances, p*size(bags,2)); 7 | for ii = 1 : size (fv,1) 8 | st = (ii-1)*numInstances+1; 9 | ed = ii * numInstances; 10 | each_fv = bags(st:ed,:); 11 | each_fv(any(isnan(each_fv)'),:) = []; 12 | 13 | fv_bag = zeros(size(each_fv, 1), p*size(each_fv,2)); 14 | for jj = 1 : size(each_fv, 1) 15 | 16 | line = each_fv(jj,:); 17 | f = zeros(1, p*size(line,2)); 18 | for kk = 1 : size(line,2) 19 | st = (kk-1)*p+1; 20 | ed = kk*p; 21 | 22 | n = fix(line(1,kk)/p_p)+1; 23 | 24 | f(1, st:ed) = matrix(n,:); 25 | end 26 | 27 | fv_bag(jj,:) = f; 28 | 29 | end 30 | 31 | fv_bag = sum(fv_bag); 32 | fv(ii,:) = fv_bag; 33 | end 34 | 35 | return; -------------------------------------------------------------------------------- /+iqvg/fun_gabor_fv.m: -------------------------------------------------------------------------------- 1 | function fv = fun_gabor_fv(patch, maxFrequency, frequencyNum, orientionsNum) 2 | 3 | minI = min(patch(:)); 4 | maxI = max(patch(:)); 5 | if abs(maxI-minI)>1e-4, 6 | a = 1/(maxI-minI); 7 | b = -a*minI; 8 | patch = a*patch+b; 9 | else 10 | patch = patch/maxI; 11 | end 12 | 13 | bank=sg_createfilterbank(size(patch), maxFrequency , frequencyNum, orientionsNum); 14 | r=sg_filterwithbank(patch,bank); 15 | gMx=sg_resp2samplematrix(r); 16 | gMx = sg_normalizesamplematrix(gMx); 17 | 18 | gfv = zeros(frequencyNum*orientionsNum*2,1); 19 | gMx = abs(gMx); 20 | gfv(1:frequencyNum * orientionsNum) = mean(mean(gMx)); % mean 21 | [N1,N2] = size(patch); 22 | gMx = reshape(gMx, N1 * N2, frequencyNum * orientionsNum); 23 | gfv( frequencyNum * orientionsNum + 1 : end) = std(gMx)'; % standard variation 24 | fv = gfv; 25 | return; -------------------------------------------------------------------------------- /+iqvg/iqvg_test.m: -------------------------------------------------------------------------------- 1 | score = IQVG( 'textImgs/img1.png') -------------------------------------------------------------------------------- /+iqvg/model.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dsoellinger/blind_image_quality_toolbox/4d12c43c77bba538f684df0b62621e9350854c43/+iqvg/model.mat -------------------------------------------------------------------------------- /+iqvg/outSVMData.m: -------------------------------------------------------------------------------- 1 | function [] = outSVMData(f) 2 | fid = fopen('f.txt', 'w'); 3 | for ii = 1 : size (f, 1) 4 | fprintf (fid, '%f ', -1); 5 | for jj = 1 : size (f, 2) 6 | fprintf (fid, '%d:', jj); 7 | fprintf (fid, '%f ', f(ii, jj)); 8 | end 9 | fprintf (fid, '\n'); 10 | end 11 | fclose (fid); 12 | return; -------------------------------------------------------------------------------- /+iqvg/readme.txt: -------------------------------------------------------------------------------- 1 | IQVG Version 1.0 2 | Copyright(c) 2014 Zhongyi Gu, Lin Zhang, and Hongyu Li 3 | All Rights Reserved. 4 | 5 | ---------------------------------------------------------------------- 6 | Permission to use, copy, or modify this software and its documentation 7 | for educational and research purposes only and without fee is here 8 | granted, provided that this copyright notice and the original authors' 9 | names appear on all copies and supporting documentation. This program 10 | shall not be used, rewritten, or adapted as the basis of a commercial 11 | software or hardware product without first obtaining permission of the 12 | authors. The authors make no representations about the suitability of 13 | this software for any purpose. It is provided "as is" without express 14 | or implied warranty. 15 | ---------------------------------------------------------------------- 16 | 17 | Please refer to the following paper 18 | 19 | Zhongyi Gu, Lin Zhang*, and Hongyu Li, Learning a blind image quality index based on visual saliency guided sampling and Gabor filtering, in: Proc. ICIP, 2013 20 | 21 | ---------------------------------------------------------------------- 22 | 23 | Input : 24 | the name of the image 25 | 26 | Output: 27 | the prediction of the quality of the image 28 | 29 | Dependency 30 | Gabor filter toolbox: 31 | http://www2.it.lut.fi/project/simplegabor/ 32 | LibSVM 33 | http://www.csie.ntu.edu.tw/~cjlin/libsvm/ 34 | 35 | Usage: 36 | score = IQVG( 'img1.bmp'); 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | -------------------------------------------------------------------------------- /+iqvg/sample_img.m: -------------------------------------------------------------------------------- 1 | %gabor_feature = sample_img (img, 7, 1, 5, 4, 5000); 2 | function [gf_feature, sal_feature] = sample_img( img, patchSize, maxFrequency, frequencyNum, orientionsNum, patchNum) 3 | 4 | import iqvg.*; 5 | 6 | if size(img,3)~=1 7 | oriMap = 0.299 * double(img(:,:,1)) + 0.587 * double(img(:,:,2)) + 0.114 * double(img(:,:,3)); 8 | end 9 | 10 | fixedPatchNum = 5000; 11 | 12 | saliencyMap = double(SDSP(img)); 13 | saliencyMap = saliencyMap./ max(saliencyMap(:)); 14 | salMap_patches = im2col(saliencyMap, [patchSize,patchSize]); 15 | J = randperm(size(salMap_patches,2)); 16 | salMap_patches = double(salMap_patches(:,J(1:min(fixedPatchNum,length(J))))); 17 | sal_feature = mean (salMap_patches); 18 | sal_feature = sal_feature'; 19 | oriMap_patches = im2col(oriMap,[patchSize,patchSize]); 20 | oriMap_patches = double(oriMap_patches(:,J(1:min(fixedPatchNum,length(J))))); 21 | 22 | [sal_value, index] = sort(sal_feature, 'descend'); 23 | 24 | fv = zeros(patchNum, 2*frequencyNum*orientionsNum); 25 | 26 | for ii = 1 : patchNum 27 | oriMap_patch = oriMap_patches(:, index(ii, 1)); 28 | oriMap_patch = reshape(oriMap_patch, patchSize, patchSize); 29 | gabor_fv = fun_gabor_fv( oriMap_patch, maxFrequency, frequencyNum, orientionsNum ); 30 | fv(ii,:) = gabor_fv; 31 | end; 32 | gf_feature = fv; 33 | return; 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | -------------------------------------------------------------------------------- /+niqe/computefeature.m: -------------------------------------------------------------------------------- 1 | function feat = computefeature(structdis) 2 | 3 | import niqe.*; 4 | 5 | % Input - MSCn coefficients 6 | % Output - Compute the 18 dimensional feature vector 7 | 8 | feat = []; 9 | 10 | 11 | 12 | [alpha betal betar] = estimateaggdparam(structdis(:)); 13 | 14 | feat = [feat;alpha;(betal+betar)/2]; 15 | 16 | shifts = [ 0 1;1 0 ;1 1;1 -1]; 17 | 18 | for itr_shift =1:4 19 | 20 | shifted_structdis = circshift(structdis,shifts(itr_shift,:)); 21 | pair = structdis(:).*shifted_structdis(:); 22 | [alpha betal betar] = estimateaggdparam(pair); 23 | meanparam = (betar-betal)*(gamma(2/alpha)/gamma(1/alpha)); 24 | feat = [feat;alpha;meanparam;betal;betar]; 25 | 26 | end 27 | -------------------------------------------------------------------------------- /+niqe/computemean.m: -------------------------------------------------------------------------------- 1 | function val = computemean(patch) 2 | 3 | val = mean2(patch); -------------------------------------------------------------------------------- /+niqe/computequality.m: -------------------------------------------------------------------------------- 1 | function quality = computequality(im,blocksizerow,blocksizecol,... 2 | blockrowoverlap,blockcoloverlap,mu_prisparam,cov_prisparam) 3 | 4 | import niqe.*; 5 | 6 | % Input 7 | % im - Image whose quality needs to be computed 8 | % blocksizerow - Height of the blocks in to which image is divided 9 | % blocksizecol - Width of the blocks in to which image is divided 10 | % blockrowoverlap - Amount of vertical overlap between blocks 11 | % blockcoloverlap - Amount of horizontal overlap between blocks 12 | % mu_prisparam - mean of multivariate Gaussian model 13 | % cov_prisparam - covariance of multivariate Gaussian model 14 | 15 | % For good performance, it is advisable to use make the multivariate Gaussian model 16 | % using same size patches as the distorted image is divided in to 17 | 18 | % Output 19 | %quality - Quality of the input distorted image 20 | 21 | % Example call 22 | %quality = computequality(im,96,96,0,0,mu_prisparam,cov_prisparam) 23 | 24 | % --------------------------------------------------------------- 25 | %Number of features 26 | % 18 features at each scale 27 | featnum = 18; 28 | %---------------------------------------------------------------- 29 | %Compute features 30 | if(size(im,3)==3) 31 | im = rgb2gray(im); 32 | end 33 | im = double(im); 34 | [row col] = size(im); 35 | block_rownum = floor(row/blocksizerow); 36 | block_colnum = floor(col/blocksizecol); 37 | 38 | im = im(1:block_rownum*blocksizerow,1:block_colnum*blocksizecol); 39 | [row col] = size(im); 40 | block_rownum = floor(row/blocksizerow); 41 | block_colnum = floor(col/blocksizecol); 42 | im = im(1:block_rownum*blocksizerow, ... 43 | 1:block_colnum*blocksizecol); 44 | window = fspecial('gaussian',7,7/6); 45 | window = window/sum(sum(window)); 46 | scalenum = 2; 47 | warning('off') 48 | 49 | feat = []; 50 | 51 | 52 | for itr_scale = 1:scalenum 53 | 54 | 55 | mu = imfilter(im,window,'replicate'); 56 | mu_sq = mu.*mu; 57 | sigma = sqrt(abs(imfilter(im.*im,window,'replicate') - mu_sq)); 58 | structdis = (im-mu)./(sigma+1); 59 | 60 | 61 | 62 | feat_scale = blkproc(structdis,[blocksizerow/itr_scale blocksizecol/itr_scale], ... 63 | [blockrowoverlap/itr_scale blockcoloverlap/itr_scale], ... 64 | @computefeature); 65 | feat_scale = reshape(feat_scale,[featnum .... 66 | size(feat_scale,1)*size(feat_scale,2)/featnum]); 67 | feat_scale = feat_scale'; 68 | 69 | 70 | if(itr_scale == 1) 71 | sharpness = blkproc(sigma,[blocksizerow blocksizecol], ... 72 | [blockrowoverlap blockcoloverlap],@computemean); 73 | sharpness = sharpness(:); 74 | end 75 | 76 | 77 | feat = [feat feat_scale]; 78 | 79 | im =imresize(im,0.5); 80 | 81 | end 82 | 83 | 84 | % Fit a MVG model to distorted patch features 85 | distparam = feat; 86 | mu_distparam = nanmean(distparam); 87 | cov_distparam = nancov(distparam); 88 | 89 | % Compute quality 90 | invcov_param = pinv((cov_prisparam+cov_distparam)/2); 91 | quality = sqrt((mu_prisparam-mu_distparam)* ... 92 | invcov_param*(mu_prisparam-mu_distparam)'); 93 | 94 | -------------------------------------------------------------------------------- /+niqe/estimateaggdparam.m: -------------------------------------------------------------------------------- 1 | function [alpha betal betar] = estimateaggdparam(vec) 2 | 3 | 4 | gam = 0.2:0.001:10; 5 | r_gam = ((gamma(2./gam)).^2)./(gamma(1./gam).*gamma(3./gam)); 6 | 7 | 8 | leftstd = sqrt(mean((vec(vec<0)).^2)); 9 | rightstd = sqrt(mean((vec(vec>0)).^2)); 10 | 11 | gammahat = leftstd/rightstd; 12 | rhat = (mean(abs(vec)))^2/mean((vec).^2); 13 | rhatnorm = (rhat*(gammahat^3 +1)*(gammahat+1))/((gammahat^2 +1)^2); 14 | [min_difference, array_position] = min((r_gam - rhatnorm).^2); 15 | alpha = gam(array_position); 16 | 17 | betal = leftstd *sqrt(gamma(1/alpha)/gamma(3/alpha)); 18 | betar = rightstd*sqrt(gamma(1/alpha)/gamma(3/alpha)); 19 | 20 | 21 | -------------------------------------------------------------------------------- /+niqe/estimatemodelparam.m: -------------------------------------------------------------------------------- 1 | function [mu_prisparam cov_prisparam] = estimatemodelparam(folderpath,... 2 | blocksizerow,blocksizecol,blockrowoverlap,blockcoloverlap,sh_th) 3 | 4 | % Input 5 | % folderpath - Folder containing the pristine images 6 | % blocksizerow - Height of the blocks in to which image is divided 7 | % blocksizecol - Width of the blocks in to which image is divided 8 | % blockrowoverlap - Amount of vertical overlap between blocks 9 | % blockcoloverlap - Amount of horizontal overlap between blocks 10 | % sh_th - The sharpness threshold level 11 | %Output 12 | %mu_prisparam - mean of multivariate Gaussian model 13 | %cov_prisparam - covariance of multivariate Gaussian model 14 | 15 | % Example call 16 | 17 | %[mu_prisparam cov_prisparam] = estimatemodelparam('pristine',96,96,0,0,0.75); 18 | 19 | 20 | %---------------------------------------------------------------- 21 | % Find the names of images in the folder 22 | current = pwd; 23 | cd(sprintf('%s',folderpath)) 24 | names = ls; 25 | names = names(3:end,:); 26 | cd(current) 27 | % --------------------------------------------------------------- 28 | %Number of features 29 | % 18 features at each scale 30 | featnum = 18; 31 | % --------------------------------------------------------------- 32 | % Make the directory for storing the features 33 | mkdir(sprintf('local_risquee_prisfeatures')) 34 | % --------------------------------------------------------------- 35 | % Compute pristine image features 36 | for itr = 1:size(names,1) 37 | itr 38 | im = imread(sprintf('%s\\%s',folderpath,names(itr,:))); 39 | if(size(im,3)==3) 40 | im = rgb2gray(im); 41 | end 42 | im = double(im); 43 | [row col] = size(im); 44 | block_rownum = floor(row/blocksizerow); 45 | block_colnum = floor(col/blocksizecol); 46 | im = im(1:block_rownum*blocksizerow, ... 47 | 1:block_colnum*blocksizecol); 48 | window = fspecial('gaussian',7,7/6); 49 | window = window/sum(sum(window)); 50 | scalenum = 2; 51 | warning('off') 52 | 53 | feat = []; 54 | 55 | 56 | for itr_scale = 1:scalenum 57 | 58 | 59 | mu = imfilter(im,window,'replicate'); 60 | mu_sq = mu.*mu; 61 | sigma = sqrt(abs(imfilter(im.*im,window,'replicate') - mu_sq)); 62 | structdis = (im-mu)./(sigma+1); 63 | 64 | 65 | 66 | feat_scale = blkproc(structdis,[blocksizerow/itr_scale blocksizecol/itr_scale], ... 67 | [blockrowoverlap/itr_scale blockcoloverlap/itr_scale], ... 68 | @computefeature); 69 | feat_scale = reshape(feat_scale,[featnum .... 70 | size(feat_scale,1)*size(feat_scale,2)/featnum]); 71 | feat_scale = feat_scale'; 72 | 73 | 74 | if(itr_scale == 1) 75 | sharpness = blkproc(sigma,[blocksizerow blocksizecol], ... 76 | [blockrowoverlap blockcoloverlap],@computemean); 77 | sharpness = sharpness(:); 78 | end 79 | 80 | 81 | feat = [feat feat_scale]; 82 | 83 | im =imresize(im,0.5); 84 | 85 | end 86 | 87 | save(sprintf('local_risquee_prisfeatures\\prisfeatures_local%d.mat',... 88 | itr),'feat','sharpness'); 89 | end 90 | 91 | 92 | 93 | %---------------------------------------------- 94 | % Load pristine image features 95 | prisparam = []; 96 | current = pwd; 97 | cd(sprintf('%s','local_risquee_prisfeatures')) 98 | names = ls; 99 | names = names(3:end,:); 100 | cd(current) 101 | for itr = 1:size(names,1) 102 | % Load the features and select the only features 103 | load(sprintf('local_risquee_prisfeatures\\%s',strtrim(names(itr,:)))); 104 | IX = find(sharpness(:) >sh_th*max(sharpness(:))); 105 | feat = feat(IX,:); 106 | prisparam = [prisparam; feat]; 107 | 108 | end 109 | %---------------------------------------------- 110 | % Compute model parameters 111 | mu_prisparam = nanmean(prisparam); 112 | cov_prisparam = nancov(prisparam); 113 | %---------------------------------------------- 114 | % Save features in the mat file 115 | save('modelparameters_new.mat','mu_prisparam','cov_prisparam'); 116 | %---------------------------------------------- 117 | -------------------------------------------------------------------------------- /+niqe/modelparameters.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dsoellinger/blind_image_quality_toolbox/4d12c43c77bba538f684df0b62621e9350854c43/+niqe/modelparameters.mat -------------------------------------------------------------------------------- /+niqe/niqe.m: -------------------------------------------------------------------------------- 1 | function [score] = niqe(image) 2 | 3 | load '+niqe/modelparameters.mat' 4 | 5 | blocksizerow = 96; 6 | blocksizecol = 96; 7 | blockrowoverlap = 0; 8 | blockcoloverlap = 0; 9 | 10 | score = niqe.computequality(image,blocksizerow,blocksizecol,blockrowoverlap,blockcoloverlap,mu_prisparam,cov_prisparam); 11 | 12 | end -------------------------------------------------------------------------------- /+niqe/readme.txt: -------------------------------------------------------------------------------- 1 | NIQE Software release. 2 | 3 | ======================================================================= 4 | -----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------ 5 | Copyright (c) 2011 The University of Texas at Austin 6 | All rights reserved. 7 | 8 | Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, 9 | modify, and distribute this code (the source files) and its documentation for 10 | any purpose, provided that the copyright notice in its entirety appear in all copies of this code, and the 11 | original source of this code, Laboratory for Image and Video Engineering (LIVE, http://live.ece.utexas.edu) 12 | and Center for Perceptual Systems (CPS, http://www.cps.utexas.edu) at the University of Texas at Austin (UT Austin, 13 | http://www.utexas.edu), is acknowledged in any publication that reports research using this code. The research 14 | is to be cited in the bibliography as: 15 | 16 | 1) A. Mittal, R. Soundararajan and A. C. Bovik, "NIQE Software Release", 17 | URL: http://live.ece.utexas.edu/research/quality/niqe.zip, 2012. 18 | 19 | 2) A. Mittal, R. Soundararajan and A. C. Bovik, "Making a Completely Blind Image Quality Analyzer", submitted to IEEE Signal Processing Letters, 2012. 20 | 21 | IN NO EVENT SHALL THE UNIVERSITY OF TEXAS AT AUSTIN BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, 22 | OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THIS DATABASE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF TEXAS 23 | AT AUSTIN HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 24 | 25 | THE UNIVERSITY OF TEXAS AT AUSTIN SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 26 | WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, 27 | AND THE UNIVERSITY OF TEXAS AT AUSTIN HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. 28 | 29 | -----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------% 30 | 31 | Author : Anish Mittal 32 | Version : 1.0 33 | 34 | The authors are with the Laboratory for Image and Video Engineering 35 | (LIVE), Department of Electrical and Computer Engineering, The 36 | University of Texas at Austin, Austin, TX. 37 | 38 | Kindly report any suggestions or corrections to mittal.anish@gmail.com 39 | 40 | ======================================================================= 41 | 42 | This is a demonstration of the Naturalness Image Quality Evaluator(NIQE) index. The algorithm is described in: 43 | 44 | A. Mittal, R. Soundararajan and A. C. Bovik, "Making a Completely Blind Image Quality Analyzer", submitted to IEEE Signal Processing Letters, 2012. 45 | 46 | You can change this program as you like and use it anywhere, but please 47 | refer to its original source (cite our paper and our web page at 48 | http://live.ece.utexas.edu/research/quality/niqe_release.zip). 49 | 50 | ======================================================================= 51 | Running on Matlab 52 | 53 | Input : A test image loaded in an array 54 | 55 | Output: A quality score of the image. Higher value represents a lower quality. 56 | 57 | Usage: 58 | 59 | 1. Load the image, for example 60 | 61 | image = imread('testimage1.bmp'); 62 | 63 | 2. Load the parameters of pristine multivariate Gaussian model. 64 | 65 | 66 | load modelparameters.mat; 67 | 68 | 69 | The images used for making the current model may be viewed at http://live.ece.utexas.edu/research/quality/pristinedata.zip 70 | 71 | 72 | 3. Initialize different parameters 73 | 74 | Height of the block 75 | blocksizerow = 96; 76 | Width of the block 77 | blocksizecol = 96; 78 | Verical overlap between blocks 79 | blocksizerow = 0; 80 | Horizontal overlap between blocks 81 | blocksizecol = 0; 82 | 83 | For good performance, it is advisable to divide the distorted image in to same size patched as used for the construction of multivariate Gaussian model. 84 | 85 | 3. Call this function to calculate the quality score: 86 | 87 | 88 | qualityscore = computequality(im,blocksizerow,blocksizecol,blockrowoverlap,blockcoloverlap,mu_prisparam,cov_prisparam) 89 | 90 | Sample execution is also shown through example.m 91 | 92 | 93 | ======================================================================= 94 | 95 | MATLAB files: (provided with release): example.m, computefeature.m, computemean.m, computequality.m, estimateaggdparam.m and estimatemodelparam.m 96 | 97 | Image Files: image1.bmp, image2.bmp, image3.bmp and image4.bmp 98 | 99 | Dependencies: Mat file: modelparameters.mat provided with release 100 | 101 | ======================================================================= 102 | 103 | Note on training: 104 | This release version of NIQE was trained on 125 pristine images with patch size set to 96X96 and sharpness threshold of 0.75. 105 | 106 | Training the model 107 | 108 | If the user wants to retrain the model using different set of pristine image or set the patch sizes to different values, he/she can do so 109 | use the following function. The images used for making the current model may be viewed at http://live.ece.utexas.edu/research/quality/pristinedata.zip 110 | 111 | Folder containing the pristine images 112 | folderpath = 'pristine' 113 | Height of the block 114 | blocksizerow = 96; 115 | Width of the block 116 | blocksizecol = 96; 117 | Verical overlap between blocks 118 | blocksizerow = 0; 119 | Horizontal overlap between blocks 120 | blocksizecol = 0; 121 | The sharpness threshold level 122 | sh_th = 0.75; 123 | 124 | 125 | [mu_prisparam cov_prisparam] = estimatemodelparam(folderpath,blocksizerow,blocksizecol,blockrowoverlap,blockcoloverlap,sh_th) 126 | ======================================================================= 127 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # BLIND IMAGE QUALITY TOOLBOX 2 | This project is a collection of algorithms for Blind Image Quality assessment in Matlab. 3 | 4 | ## Hint: 5 | - IQVG requires some Libsvm-Mex files which are available here: https://github.com/cjlin1/libsvm/tree/master 6 | Don't forget to add the library to the path (see computeQualityMetrics.m) 7 | - DIIVINE requires a special version of Matlab which is available here: https://github.com/gregfreeman/libsvm/tree/new_matlab_interface 8 | Don't forget to add the library to the path (see computeQualityMetrics.m) 9 | - Depending on your environment setup it might be necessary to modify the path to all libsvm executables (see IQVG.m, brisquescore.m) 10 | 11 | ## BIQAA 12 | Salvador Gabarda and Gabriel Cristóbal, "Blind image quality assessment through anisotropy," J. Opt. Soc. Am. A 24, B42-B51 (2007) 13 | 14 | ## BIQI 15 | A. K. Moorthy and A. C. Bovik, "A Modular Framework for Constructing Blind 16 | Universal Quality Indices", submitted to IEEE Signal Processing Letters (2009). 17 | 18 | A. K. Moorthy and A. C. Bovik, "BIQI Software Release", 19 | http://live.ece.utexas.edu/research/quality/biqi.zip, 2009. 20 | 21 | ## BLIINDS 2 22 | M.A Saad and A. C. Bovik, "Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain, " IEEE Transactions on Image Processing, vol. pp, no. 99, pp. 1, Mar. 2012. (early access) 23 | 24 | M.A. Saad, A. C. Bovik, and C. Charrier, "DCT Statistics Model-Based Blind Image Quality Assessment," Proceedings of the IEEE International Conference on image Processing (ICIP), pp. 3093-3096, Sep. 2011. 25 | 26 | ## BRISQUE 27 | A. Mittal, A. K. Moorthy and A. C. Bovik, "BRISQUE Software Release", 28 | http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip, 2011 29 | 30 | A. Mittal, A. K. Moorthy and A. C. Bovik, "No Reference Image Quality Assessment in the Spatial Domain" submitted 31 | 32 | ## DIIVINE 33 | A. K. Moorthy and A. C. Bovik, "Blind Image Quality Assessment: From Natural 34 | Scene Statistics to Perceptual Quality", IEEE Transactions on Image Processing, to appear (2011). 35 | 36 | A. K. Moorthy and A. C. Bovik, "DIVINE Software Release", 37 | http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip, 2010 38 | 39 | ## IQVG 40 | Zhongyi Gu, Lin Zhang*, and Hongyu Li, Learning a blind image quality index based on visual saliency guided sampling and Gabor filtering, in: Proc. ICIP, 2013 41 | 42 | ## NIQE 43 | A. Mittal, R. Soundararajan and A. C. Bovik, "NIQE Software Release", 44 | http://live.ece.utexas.edu/research/quality/niqe.zip, 2012. 45 | 46 | A. Mittal, R. Soundararajan and A. C. Bovik, "Making a Completely Blind Image Quality Analyzer", submitted to IEEE Signal Processing Letters, 2012. 47 | -------------------------------------------------------------------------------- /computeQualityMetrics.m: -------------------------------------------------------------------------------- 1 | function [results] = computeQualityMetrics(image) 2 | 3 | % Anisotrophy Test 4 | [gray,rgb] = biqaa.blindimagequality(image,8,6,0,'degree'); 5 | results.biqaa_gray = gray; 6 | results.biqaa_rgb = rgb; 7 | 8 | % BIQI Test 9 | results.biqi = biqi.biqi(image); 10 | 11 | % Bliinds2 Test 12 | results.bliinds = bliinds2.bliinds2_score(image); 13 | 14 | % IQVG Test 15 | addpath('/Users/dsoellinger/Documents/git/uni/Matlab Toolbox/cjlin1_svnlib/libsvm/matlab'); 16 | results.iqvg = iqvg.IQVG(image); 17 | rmpath('/Users/dsoellinger/Documents/git/uni/Matlab Toolbox/cjlin1_svnlib/libsvm/matlab'); 18 | 19 | % NIQE Test 20 | results.niqe = niqe.niqe(image); 21 | 22 | % DIVINE Test 23 | addpath('/Users/dsoellinger/Documents/git/uni/Matlab Toolbox/gregfreeman_libsvm/libsvm/matlab'); 24 | results.divine = divine.divine(image); 25 | rmpath('/Users/dsoellinger/Documents/git/uni/Matlab Toolbox/gregfreeman_libsvm/libsvm/matlab'); 26 | 27 | % BRISQUE Test 28 | % Requires libsvm 3.22. Make sure that it is installed. 29 | results.brisque = brisque.brisquescore(image); 30 | 31 | 32 | end 33 | -------------------------------------------------------------------------------- /examples/image.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dsoellinger/blind_image_quality_toolbox/4d12c43c77bba538f684df0b62621e9350854c43/examples/image.jpg -------------------------------------------------------------------------------- /examples/simpleExample.m: -------------------------------------------------------------------------------- 1 | 2 | image = imread('image.jpg'); 3 | 4 | result = computeQualityMetrics(image) --------------------------------------------------------------------------------