[1] E. Agustsson, D. Minnen, N. Johnston, J. Balle, S. J. Hwang, and G. Toderici, "Scale-space flow for end-to-end optimized video compression," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020, pp. 8503-8512.
[2] R. Yang, Y. Yang, J. Marino, and S. Mandt, "Hierarchical autoregressive modeling for neural video compression," 2020. [Online]. Available: https://arxiv.org/pdf/2010.10258.pdf
[3] O. Rippel, A. G. Anderson, K. Tatwawadi, S. Nair, C. Lytle, and L. Bourdev, "Elf-vc: Efficient learned flexible-rate video coding," 2021. [Online]. Available: https://arxiv.org/abs/2104.14335
[4] J. Li, B. Li, and Y. Lu, “Deep contextual video compression,” in Advances in Neural Information Processing Systems, 2021, pp. 18 114–18 125.
[5] A. Golinski, R. Pourreza, Y. Yang, G. Sautiere, and T. S. Cohen, "Feedback recurrent autoencoder for video compression," in Proceedings of the Asian Conference on Computer Vision, 2020.
[6] S. Zhang, M. Mrak, L. Herranz, M. G. Blanch, S. Wan, and F. Yang, "Dvc-p: Deep video compression with perceptual optimizations," in 2021 International Conference on Visual Communications and Image Processing (VCIP). IEEE, 2021, pp. 1-5.
[7] F. Mentzer, E. Agustsson, J. Balle, D. Minnen, N. Johnston, and G. Toderici, “Neural video compression using gans for detail synthesis and propagation,” in European Conference on Computer Vision, 2022.
[8] R. Yang, L. Van Gool, and R. Timofte, "Perceptual learned video compression with recurrent conditional gan," arXiv preprint arXiv:2109.03082, vol. 1, 2021.
[9] V. Veerabadran, R. Pourreza, A. Habibian, and T. Cohen, "Adversarial distortion for learned video compression," 2021. [Online]. Available: https://arxiv.org/pdf/2004.09508.pdf
[10] Y. Wang, P. Bilinski, F. Bremond, and A. Dantcheva, "G³an: Disentangling appearance and motion for video generation," in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[11] Y. Blau and T. Michaeli, “Rethinking lossy compression: The rate-distortion-perception tradeoff,” in International Conference on Machine Learning. PMLR, 2019, pp. 675–685.
[12] E. Agustsson, M. Tschannen, F. Mentzer, R. Timofte, and L. Van Gool, "Generative adversarial networks for extreme learned image compression," in Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 221-231.
[13] J. Balle, V. Laparra, and E. P. Simoncelli, "End-to-end optimized image compression," in 5th International Conference on Learning Representations, 2017.
[14] L. Theis, W. Shi, A. Cunningham, and F. Huszár, "Lossy image compression with compressive autoencoders," in 5th International Conference on Learning Representations, 2017.
[15] F. Mentzer, E. Agustsson, M. Tschannen, R. Timofte, and L. V. Gool, "Conditional probability models for deep image compression," in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[16] G. Zhang, J. Qian, J. Chen, and A. Khisti, “Universal rate-distortion-perception representations for lossy compression,” in Advances in Neural Information Processing Systems, 2021, pp. 11517–11529.
[17] C. Gao, T. Xu, D. He, Y. Wang, and H. Qin, "Flexible neural image compression via code editing," Advances in Neural Information Processing Systems, vol. 35, pp. 12-184-12-196, 2022.
[18] F. Mentzer, G. Toderici, M. Tschannen, and E. Agustsson, "High-fidelity generative image compression," in Advances in Neural Information Processing Systems, 2020.
[19] E. Agustsson, M. Tschannen, F. Mentzer, R. Timofte, and L. V. Gool, "Generative adversarial networks for extreme learned image compression," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 221-231.
[20] G. Lu, C. Cai, X. Zhang, L. Chen, W. Ouyang, D. Xu, and Z. Gao, "Content adaptive and error propagation aware deep video compression," in Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part II 16. Springer, 2020, pp. 456-472.