├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Tim 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Learning-based-Image-Video-Compression 2 | Recent papers and codes related to learning-based image/video compression. Mainly focus on top venues of machine learning community. 3 | 4 | ---- 5 | 6 | ## Learning-based Image Compression 7 | 8 | ### 2016 9 | 10 | 1. [Google] G. Toderici, S. M. O'Malley, S. J. Hwang, D. Vincent, D. Minnen, S. Baluja, M. Covell, R. Sukthankar: Variable rate image compression with recurrent neural networks. ICLR 2016. [[Paper](https://arxiv.org/abs/1511.06085)] 11 | 2. [DeepMind] K. Gregor, F. Besse, D. J. Rezende, I. Danihelka, D. Wierstra: Towards conceptual compression. NIPS 2016. [[Paper](https://arxiv.org/abs/1604.08772)] 12 | 13 | ### 2017 14 | 15 | 1. [Google] G. Toderici, D. Vincent, N. Johnston, S. J. Hwang, D. Minnen, J. Shor, M. Covell: Full resolution image compression with recurrent neural networks. CVPR 2017. [[Paper](https://arxiv.org/abs/1608.05148)] 16 | 2. [NYU] J. Ballé, V. Laparra, E. P. Simoncelli: End-to-end optimized image compression. ICLR 2017. [[Paper](https://arxiv.org/abs/1611.01704)] 17 | 3. [Twitter] L. Theis, W. Shi, A. Cunningham, F. Huszár: Lossy image compression with compressive autoencoders. ICLR 2017. [[Paper](https://arxiv.org/abs/1703.00395)] 18 | 4. [INRIA] T. Dumas, A. Roumy, C. Guillemot: Image compression with stochastic winner-take-all auto-encoder. ICASSP 2017. [[Paper](https://ieeexplore.ieee.org/document/7952409/)] 19 | 5. [WaveOne] O. Rippel, L. Bourdev: Real-time adaptive image compression. ICML 2017. [[Paper](https://arxiv.org/abs/1705.05823)] 20 | 6. [Dartmouth] M. H. Baig, V. Koltun, L. Torresani: Learning to Inpaint for Image Compression. NIPS 2017. [[Paper](https://arxiv.org/abs/1709.08855)] 21 | 22 | ### 2018 23 | 24 | 1. [Google] N. Johnston, D. Vincent, D. Minnen, M. Covell, S. Singh, T. Chinen, S. J. Hwang, J. Shor, G. Toderici: Improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks. CVPR 2018. [[Paper](https://arxiv.org/abs/1703.10114)] 25 | 2. [HKPU] M. Li, W. Zuo, S. Gu, D. Zhao, D. Zhang: Learning convolutional networks for content-weighted image compression. CVPR 2018. [[Paper](https://arxiv.org/abs/1703.10553)] 26 | 3. [ETHZ] F. Mentzer, E. Agustsson, M. Tschannen, R. Timofte, L. Van Gool: Conditional probability models for deep image compression. CVPR 2018. [[Paper](https://arxiv.org/abs/1801.04260)] 27 | 4. [Technion] T.R. Shaham, T. Michaeli: Deformation Aware Image Compression. CVPR 2018. [[Paper](https://arxiv.org/abs/1804.04593)] 28 | 5. [INRIA] T. Dumas, Aline, Roumy, C. Guillemot: Autoencoder based Image Compression: Can the Learning be Quantization Independent? ICASSP 2018. [[Paper](https://arxiv.org/abs/1802.09371)] 29 | 6. [Google] D. Minnen, G. Toderici, S. Singh, S. J. Hwang, M. Covell: Image-Dependent Local Entropy Models for Learned Image Compression. ICIP 2018. [[Paper](https://arxiv.org/abs/1805.12295)] 30 | 7. [Google] T. Chinen, J. Ballé, C. Gu, S. J. Hwang, S. Ioffe, N. Johnston, T. Leung, D. Minnen, S. O'Malley, C. Rosenberg, G. Toderici Towards A Semantic Perceptual Image Metric. ICIP 2018. [[Paper](https://arxiv.org/abs/1808.00447)] 31 | 8. [RIT/PSU] A. G. Ororbia, A. Mali, J. Wu, S. O'Connell, D. Miller, C. L. Giles: Learned Neural Iterative Decoding for Lossy Image Compression Systems. ArXiv. [[Paper](https://arxiv.org/abs/1803.05863)] 32 | 9. [SFU/Google] M. Akbari, J. Liang, J. Han: DSSLIC: Deep Semantic Segmentation-based Layered Image Compression. ArXiv. [[Paper](https://arxiv.org/abs/1806.03348)] 33 | 10. [ETHZ] F. Mentzer, E. Agustsson, M. Tschannen, R. Timofte, L. V. Gool: Practical Full Resolution Learned Lossless Image Compression. ArXiv. [[Paper](https://arxiv.org/abs/1811.12817)] 34 | 35 | ---- 36 | 37 | ## Learning-based Video Compression 38 | 39 | ### 2018 40 | 41 | 1. [USTC] Z. Chen, T. He, X. Jin, F. Wu: Learning for video compression. IEEE Trans. on CSVT 2018. [[Paper](https://ieeexplore.ieee.org/abstract/document/8610323)] 42 | 2. [UTEXAS] C. Wu, N. Singhal, P. Krähenbühl: Video Compression through Image Interpolation. ECCV 2018. [[Paper](http://openaccess.thecvf.com/content_ECCV_2018/html/Chao-Yuan_Wu_Video_Compression_through_ECCV_2018_paper.html)] 43 | 3. [Disney] J. Han, S. Lombardo, C. Schroers, S. Mandt: Deep Probabilistic Video Compression. ArXiv. [[Paper](https://arxiv.org/abs/1810.02845)] 44 | 4. [WaveOne] O. Rippel, S. Nair, C. Lew, S. Branson, A. G. Anderson, L. Bourdev: Learned Video Compression. ArXiv. [[Paper](https://arxiv.org/abs/1811.06981)] 45 | 5. [SJTU/Sydney] G. Lu, W. Ouyang, D. Xu, X. Zhang, C. Cai, Z. Gao: DVC: An End-to-end Deep Video Compression Framework. ArXiv. [[Paper](https://arxiv.org/abs/1812.00101)] 46 | 6. [UTEXAS] S. Kim, J. S. Park, C. G. Bampis, J. Lee, M. K. Markey, A. G. Dimakis, A. C. Bovik: Adversarial Video Compression Guided by Soft Edge Detection. ArXiv. [[Paper](https://arxiv.org/abs/1811.10673)] 47 | --------------------------------------------------------------------------------