Recent papers and codes related to learning-based image/video compression. Mainly focus on top venues of machine learning community.
- [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]
- [DeepMind] K. Gregor, F. Besse, D. J. Rezende, I. Danihelka, D. Wierstra: Towards conceptual compression. NIPS 2016. [Paper]
- [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]
- [NYU] J. Ballé, V. Laparra, E. P. Simoncelli: End-to-end optimized image compression. ICLR 2017. [Paper]
- [Twitter] L. Theis, W. Shi, A. Cunningham, F. Huszár: Lossy image compression with compressive autoencoders. ICLR 2017. [Paper]
- [INRIA] T. Dumas, A. Roumy, C. Guillemot: Image compression with stochastic winner-take-all auto-encoder. ICASSP 2017. [Paper]
- [WaveOne] O. Rippel, L. Bourdev: Real-time adaptive image compression. ICML 2017. [Paper]
- [Dartmouth] M. H. Baig, V. Koltun, L. Torresani: Learning to Inpaint for Image Compression. NIPS 2017. [Paper]
- [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]
- [HKPU] M. Li, W. Zuo, S. Gu, D. Zhao and D. Zhang: Learning convolutional networks for content-weighted image compression. CVPR 2018. [Paper]
- [ETHZ] F. Mentzer, E. Agustsson, M. Tschannen, R. Timofte and L. Van Gool: Conditional probability models for deep image compression. CVPR 2018. [Paper]
- [Technion] T.R. Shaham and T. Michaeli: Deformation Aware Image Compression. CVPR 2018. [Paper]