Learning-based-Image-Video-Compression

Recent papers and codes related to learning-based image/video compression. Mainly focus on top venues of machine learning community.

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Learning-based Image Compression

2016

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]
2. [DeepMind] K. Gregor, F. Besse, D. J. Rezende, I. Danihelka, D. Wierstra: Towards conceptual compression. NIPS 2016. [Paper]

2017

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]
2. [NYU] J. Ballé, V. Laparra, E. P. Simoncelli: End-to-end optimized image compression. ICLR 2017. [Paper]
3. [Twitter] L. Theis, W. Shi, A. Cunningham, F. Huszár: Lossy image compression with compressive autoencoders. ICLR 2017. [Paper]
4. [INRIA] T. Dumas, A. Roumy, C. Guillemot: Image compression with stochastic winner-take-all auto-encoder. ICASSP 2017. [Paper]
5. [WaveOne] O. Rippel, L. Bourdev: Real-time adaptive image compression. ICML 2017. [Paper]
6. [Dartmouth] M. H. Baig, V. Koltun, L. Torresani: Learning to Inpaint for Image Compression. NIPS 2017. [Paper]

2018

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]
2. [HKPU] M. Li, W. Zuo, S. Gu, D. Zhao, D. Zhang: Learning convolutional networks for content-weighted image compression. CVPR 2018. [Paper]
3. [ETHZ] F. Mentzer, E. Agustsson, M. Tschannen, R. Timofte, L. Van Gool: Conditional probability models for deep image compression. CVPR 2018. [Paper]
4. [Technion] T.R. Shaham, T. Michaeli: Deformation Aware Image Compression. CVPR 2018. [Paper]
5. [INRIA] T. Dumas, Aline, Roumy, C. Guillemot: Autoencoder based Image Compression: Can the Learning be Quantization Independent? ICASSP 2018. [Paper]
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]
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]
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]
9. [SFU/Google] M. Akbari, J. Liang, J. Han: DSSLIC: Deep Semantic Segmentation-based Layered Image Compression. ArXiv. [Paper]
10. [ETHZ] F. Mentzer, E. Agustsson, M. Tschannen, R. Timofte, L. V. Gool: Practical Full Resolution Learned Lossless Image Compression. ArXiv. [Paper]

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Learning-based Video Compression

2018

1. [USTC] Z. Chen, T. He, X. Jin, F. Wu: Learning for video compression. IEEE Trans. on CSVT 2018. [Paper]
2. [UTEXAS] C. Wu, N. Singhal, P. Krähenbühl: Video Compression through Image Interpolation. ECCV 2018. [Paper]
3. [Disney] J. Han, S. Lombardo, C. Schroers, S. Mandt: Deep Probabilistic Video Compression. ArXiv. [Paper]
4. [WaveOne] O. Rippel, S. Nair, C. Lew, S. Branson, A. G. Anderson, L. Bourdev: Learned Video Compression. ArXiv. [Paper]
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]
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]