Learning-based-Image-Video-Compression

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Learning-based-Image-Video-Compression

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

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# 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](https://arxiv.org/abs/1511.06085)]
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)]

### 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](https://arxiv.org/abs/1608.05148)]
2. [NYU] J. Ballé, V. Laparra, E. P. Simoncelli: End-to-end optimized image compression. ICLR 2017. [[Paper](https://arxiv.org/abs/1611.01704)]
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)]
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/)]
5. [WaveOne] O. Rippel, L. Bourdev: Real-time adaptive image compression. ICML 2017. [[Paper](https://arxiv.org/abs/1705.05823)]
6. [Dartmouth] M. H. Baig, V. Koltun, L. Torresani: Learning to Inpaint for Image Compression. NIPS 2017. [[Paper](https://arxiv.org/abs/1709.08855)]

### 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](https://arxiv.org/abs/1703.10114)]
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)]
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)]
4. [Technion] T.R. Shaham, T. Michaeli: Deformation Aware Image Compression. CVPR 2018. [[Paper](https://arxiv.org/abs/1804.04593)]
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)]
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)]
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)]
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)]
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)]
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)]

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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](https://ieeexplore.ieee.org/abstract/document/8610323)]
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)]
3. [Disney] J. Han, S. Lombardo, C. Schroers, S. Mandt: Deep Probabilistic Video Compression. ArXiv. [[Paper](https://arxiv.org/abs/1810.02845)]
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)]
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)]
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)]

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