└── README.md


/README.md:
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 1 | # Single Image Super-Resolution Quality Assessment: A Real-World Dataset, Subjective Studies, and An Objective Metric
 2 | This is the brief description of *RealSRQ* dataset and *KLTSRQA* software. You can change our program as you like and use it for academic, but please refer to its original source and cite our paper.
 3 | 
 4 | # Table of content
 5 | 1. [Link](#Link)
 6 | 2. [Abstract](#Abstract)
 7 | 3. [Download](#Download)
 8 | 4. [Requirement](#Requirement)
 9 | 5. [Questions](#Questions)
10 | 6. [Citation](#Citation)
11 | 
12 | # Link
13 | - Title: Single Image Super-Resolution Quality Assessment: A Real-World Dataset, Subjective Studies, and An Objective Metric 
14 | - Publish: IEEE Transactions on Image Processing, 2022
15 | - Authors: Qiuping Jiang, Zhentao Liu, Ke Gu, Feng Shao, Xinfeng Zhang, Hantao Liu, Weisi Lin
16 | - Institution: The School of Information Science and Engineering, Ningbo University
17 | - Paper: [2022-TIP-RealSRQA](https://ieeexplore.ieee.org/document/9727079)
18 | 
19 | # Abstract
20 | Numerous single image super-resolution (SISR) algorithms have been proposed during the past years to reconstruct a high-resolution (HR) image from its low-resolution (LR) observation. However, how to fairly compare the performance of different SISR algorithms/results remains a challenging problem. So far, the lack of comprehensive human subjective study on large-scale real-world SISR datasets and accurate objective SISR quality assessment metrics makes it unreliable to truly understand the performance of different SISR algorithms. We in this paper make efforts to tackle these two issues. Firstly, we construct a real-world SISR quality dataset (i.e., RealSRQ) and conduct human subjective studies to compare the performance of the representative SISR algorithms. Secondly, we propose a new objective metric, i.e., KLTSRQA, based on the Karhunen-Loeve Transform (KLT) to evaluate the quality of SISR images in a no-reference (NR) manner. Experiments on our constructed RealSRQ
21 | and the latest synthetic SISR quality dataset (i.e., QADS) have demonstrated the superiority of our proposed KLTSRQA metric, achieving higher consistency with human subjective scores than relevant existing NR image quality assessment (NR-IQA) metrics.
22 | 
23 | # Download
24 | You can download our constructed dataset and proposed software from  
25 | BaiduYun Disk: [*RealSRQ-KLTSRQA-released*](https://pan.baidu.com/s/15ZgfpW1b2_gMAETBUeszSg) (key:1121)  
26 | Google Drive: [*RealSRQ-KLTSRQA-released*](https://drive.google.com/drive/folders/1VTMBmxkZkZtbv_ONMME-7TRyfXNfRw9p?usp=sharing)
27 | 
28 | Important note: Before you use our dataset, please read the README.txt file carefully especially for the data structure of BT-score.mat. I believe this will help you understand our work.
29 | 
30 | # Requirement
31 | Matlab R2019a 
32 | 
33 | Important Note: other versions may lead to some errors.
34 | 
35 | # Questions
36 | If you have any questions of this repo or our paper, please feel free to contact with the authors: jiangqiuping@nbu.edu.cn, zhentaoliu0319@163.com.
37 | 
38 | # Citation
39 | If you find this work is useful for you, please cite the following paper:
40 | 
41 |     @ARTICLE{RealSRQ-KLTSRQA,
42 |     author={Jiang, Qiuping and Liu, Zhentao and Gu, Ke and Shao, Feng and Zhang, Xinfeng and Liu, Hantao and Lin, Weisi},
43 |     journal={IEEE Transactions on Image Processing}, 
44 |     title={Single Image Super-Resolution Quality Assessment: A Real-World Dataset, Subjective Studies, and an Objective Metric}, 
45 |     year={2022},
46 |     volume={31},
47 |     number={},
48 |     pages={2279-2294},
49 |     doi={10.1109/TIP.2022.3154588}}
50 | 


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