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Incorporating diffusion models in the image compression domain has the potential to produce realistic and detailed reconstructions, especially at extremely low bitrates.
Shannon, C.E.: A Mathematical Theory of Communication. The Bell System Technical Journal 27
1948
Earlier work this paper cites.
Pasco, R.C.: Source coding algorithms for fast data compression (1976)
1976
Earlier work this paper cites.
Wallace, G.: The JPEG still picture compression standard. IEEE Transactions on Consumer Electronics 38
1992
Earlier work this paper cites.
Kodak: PhotoCD PCD0992 (1993)
1993
Earlier work this paper cites.
Glickman, M.E.: A Comprehensive Guide to Chess Ratings. American Chess Journal 3
1995
Earlier work this paper cites.
Mentzer, F., Toderici, G., Tschannen, M., Agustsson, E.: High-Fidelity Generative Image Compression (Oct 2020). https://doi.org/10.48550/arXiv.2006.09965
2006
Earlier work this paper cites.
Kingma, D.P., Welling, M.: Auto-Encoding Variational Bayes. In: 2nd International Conference on Learning Representations, ICLR (2014)
2014
Earlier work this paper cites.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep Unsupervised Learning using Nonequilibrium Thermodynamics. In: Proceedings of the 32nd International Conference on Machine Learning. pp. 2256–2265. PMLR (Jun 2015)
2015
Earlier work this paper cites.
Ballé, J., Laparra, V., Simoncelli, E.P.: End-to-end Optimized Image Compression (Mar 2017). https://doi.org/10.48550/arXiv.1611.01704
2017
Earlier work this paper cites.
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Ballé, J., Minnen, D., Singh, S., Hwang, S.J., Johnston, N.: Variational image compression with a scale hyperprior (May 2018). https://doi.org/10.48550/arXiv.1802.01436
2018
Earlier work this paper cites.
Minnen, D., Ballé, J., Toderici, G.: Joint Autoregressive and Hierarchical Priors for Learned Image Compression (Sep 2018). https://doi.org/10.48550/arXiv.1809.02736
2018
Earlier work this paper cites.
Agustsson, E., Tschannen, M., Mentzer, F., Timofte, R., Gool, L.V.: Generative Adversarial Networks for Extreme Learned Image Compression. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 221–231 (2019)
2019
Earlier work this paper cites.
Blau, Y., Michaeli, T.: Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff. In: Proceedings of the 36th International Conference on Machine Learning. pp. 675–685. PMLR (May 2019)
2019
Earlier work this paper cites.
Xue, T., Chen, B., Wu, J., Wei, D., Freeman, W.T.: Video Enhancement with Task-Oriented Flow. International Journal of Computer Vision 127
2019
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising Diffusion Probabilistic Models. In: Advances in Neural Information Processing Systems. vol. 33, pp. 6840–6851. Curran Associates, Inc. (2020)
2020
Cited alongside, same era.
2021
Cited alongside, same era.
Dhariwal, P., Nichol, A.Q.: Diffusion Models Beat GANs on Image Synthesis. In: Advances in Neural Information Processing Systems (Nov 2021)
2021
Cited alongside, same era.
Song, J., Meng, C., Ermon, S.: Denoising Diffusion Implicit Models. In: International Conference on Learning Representations (Jan 2021)
2021
Cited alongside, same era.
Challenge on Learned Image Compression (2022)
2022
Careil, M., Muckley, M.J., Verbeek, J., Lathuilière, S.: Towards image compression with perfect realism at ultra-low bitrates (Oct 2023). https://doi.org/10.48550/arXiv.2310.10325
2023
Later among the works it cites.
Chen, J., Yu, J., Ge, C., Yao, L., Xie, E., Wu, Y., Wang, Z., Kwok, J., Luo, P., Lu, H., Li, Z.: PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis (Oct 2023). https://doi.org/10.48550/arXiv.2310.00426
2023
Later among the works it cites.
Goose, N.F., Petersen, J., Wiggers, A., Xu, T., Sautière, G.: Neural Image Compression with a Diffusion-Based Decoder (Jan 2023). https://doi.org/10.48550/arXiv.2301.05489
2023
Later among the works it cites.
Hendrycks, D., Gimpel, K.: Gaussian error linear units (gelus) (2023)
2023
Later among the works it cites.
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Cited alongside, same era.
Forsgren, S., Martiros, H.: Riffusion - Stable diffusion for real-time music generation (2022), https://riffusion.com/about
2022
Cited alongside, same era.
Graikos, A., Malkin, N., Jojic, N., Samaras, D.: Diffusion Models as Plug-and-Play Priors. Advances in Neural Information Processing Systems 35
2022
Cited alongside, same era.
He, D., Yang, Z., Peng, W., Ma, R., Qin, H., Wang, Y.: ELIC: Efficient Learned Image Compression With Unevenly Grouped Space-Channel Contextual Adaptive Coding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5718–5727 (2022)
2022
Cited alongside, same era.
Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.Y., Ermon, S.: SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations. In: International Conference on Learning Representations (2022)
2022
Cited alongside, same era.
Qian, Y., Lin, M., Sun, X., Tan, Z., Jin, R.: Entroformer: A Transformer-based Entropy Model for Learned Image Compression (Mar 2022). https://doi.org/10.48550/arXiv.2202.05492
2022
Cited alongside, same era.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-Resolution Image Synthesis With Latent Diffusion Models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10684–10695 (2022)
2022
Cited alongside, same era.
Agustsson, E., Minnen, D., Toderici, G., Mentzer, F.: Multi-Realism Image Compression with a Conditional Generator (Mar 2023). https://doi.org/10.48550/arXiv.2212.13824
2023
Cited alongside, same era.
2023
Later among the works it cites.
Hoogeboom, E., Heek, J., Salimans, T.: Simple diffusion: End-to-end diffusion for high resolution images. In: Proceedings of the 40th International Conference on Machine Learning. ICML’23, vol. 202, pp. 13213–13232. JMLR.org, Honolulu, Hawaii, USA (Jul 2023)
2023
Later among the works it cites.
Jabri, A., Fleet, D.J., Chen, T.: Scalable adaptive computation for iterative generation. In: Proceedings of the 40th International Conference on Machine Learning. ICML’23, vol. 202, pp. 14569–14589. JMLR.org, Honolulu, Hawaii, USA (Jul 2023)
2023
Later among the works it cites.
Ke, B., Obukhov, A., Huang, S., Metzger, N., Daudt, R.C., Schindler, K.: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation (Dec 2023). https://doi.org/10.48550/arXiv.2312.02145
2023
Later among the works it cites.
Lei, E., Uslu, Y.B., Hassani, H., Bidokhti, S.S.: Text + Sketch: Image Compression at Ultra Low Rates (Jul 2023). https://doi.org/10.48550/arXiv.2307.01944
2023
Later among the works it cites.
Muckley, M.J., El-Nouby, A., Ullrich, K., Jegou, H., Verbeek, J.: Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models. In: Proceedings of the 40th International Conference on Machine Learning. pp. 25426–25443. PMLR (Jul 2023)
2023
Later among the works it cites.
Stein, G., Cresswell, J.C., Hosseinzadeh, R., Sui, Y., Ross, B.L., Villecroze, V., Liu, Z., Caterini, A.L., Taylor, J.E.T., Loaiza-Ganem, G.: Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models (Oct 2023). https://doi.org/10.48550/arXiv.2306.04675
2023
Later among the works it cites.
Yang, R., Mandt, S.: Lossy Image Compression with Conditional Diffusion Models. Advances in Neural Information Processing Systems 36
2023
Later among the works it cites.
2023
Later among the works it cites.
Bachard, T., Bordin, T., Maugey, T.: Coclico: Extremely low bitrate image compression based on clip semantic and tiny color map. In: Picture Coding Symposium 2024 (2024)
2024
Closest in time.