└── README.md /README.md: -------------------------------------------------------------------------------- 1 | ## CVPR-CLIC2019-Challenge 2 | 3 | #### CVPR 2019 Workshop and Challenge on Learned Image Compression 4 | 5 | * A Compression Objective and a Cycle Loss for Neural Image Compression [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 6 | 7 | * A Better Color Space Conversion Based on Learned Variances For Image Compression [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 8 | 9 | * A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 10 | 11 | * Efficient Learning Based Sub-pixel Image Compression [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 12 | 13 | * Attention Based Image Compression Post-Processing Convolutional Neural Network [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 14 | 15 | * End-to-end Optimized Image Compression with Attention Mechanism [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 16 | 17 | * Practical Stacked Non-local Attention Modules for Image Compression [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 18 | 19 | * Compressing Weight-updates for Image Artifacts Removal Neural Networks [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 20 | 21 | * Compressing Weight-updates for Image Artifacts Removal Neural Networks [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 22 | 23 | * Learned Prior Information for Image Compression [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 24 | 25 | * Deep Residual Learning for Image Compression [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 26 | 27 | * Decoder Side Color Image Quality Enhancement using a Wavelet Transform based 3-stage Convolutional Neural Network [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 28 | 29 | * Extended End-to-End optimized Image Compression Method based on a Context-Adaptive Entropy Model [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 30 | 31 | * Learning-Based Image Compression using Convolutional Autoencoder and Wavelet Decomposition [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 32 | 33 | * Learned Image Compression with Residual Coding [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 34 | 35 | * Learning Patterns of Latent Residual for Improving Video Compression [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 36 | 37 | * Learned Image Restoration for VVC Intra Coding [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 38 | 39 | * Multi-scale and Context-adaptive Entropy Model for Image Compression [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 40 | 41 | * Practical Stacked Non-local Attention Modules for Image Compression [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 42 | 43 | * RDO-based Secondary Prediction Scheme for Image Compression [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 44 | 45 | * Variational Autoencoder Based Image Compression with Pyramidal Features and Context Entropy Model [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 46 | 47 | * VimicroABCnet: An Image Coder Combining A Better Color Space Conversion Algorithm and A Post Enhancing Network [[PDF]](https://github.com/mdcnn/Image-and-Video-Compression-Resource/tree/master/PDF) 48 | 49 | ## CVPR-CLIC2018-Challenge 50 | 51 | #### CVPR 2018 Workshop and Challenge on Learned Image Compression [[Web]](http://openaccess.thecvf.com/content_cvpr_2018_workshops/w50/html/) 52 | 53 | * An Autoencoder-based Learned Image Compressor: Description of Challenge Proposal by NCTU 54 | 55 | * Autoencoders with Variable Sized Latent Vector for Image Compression 56 | 57 | * An Implementation of Picture Compression with A CNN-based Auto-encoder 58 | 59 | * Block-optimized Variable Bit Rate Neural Image Compression 60 | 61 | * BlockCNN: A Deep Network for Artifact Removal and Image Compression 62 | 63 | * CNN-Optimized Image Compression with Uncertainty based Resource Allocation 64 | 65 | * Compression artifact removal using multi-scale reshuffling convolutional network 66 | 67 | * Combine Traditional Compression MethodWith Convolutional Neural Networks 68 | 69 | * Deep Image Compression via End-to-End Learning 70 | 71 | * Decoder Side Image Quality Enhancement exploiting Inter-channel Correlation in a 3-stage CNN: Submission to CLIC 2018 72 | 73 | * Extreme Learned Image Compression with GANs 74 | 75 | * Fully Convolutional Model for Variable Bit Length and Lossy High Density Compression of Mammograms 76 | 77 | * Image compression with xvc 78 | 79 | * Joint denoising and decompression using CNN regularization 80 | 81 | * Learned Compression Artifact Removal by Deep Residual Networks 82 | 83 | * Learning Compressible 360? Video Isomers 84 | 85 | * Perceptually optimized low bit-rate image encoding 86 | 87 | * Performance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compression 88 | 89 | * Variational Autoencoder for Low Bit-rate Image Compression 90 | 91 | * Wide-activated Deep Residual Networks based Restoration for BPG-compressed Images 92 | 93 | * YASO 94 | --------------------------------------------------------------------------------