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We propose a GAN-based image compression method working at extremely low bitrates below 0.1bpp.
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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) , June 2018
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M. Tschannen, E. Agustsson, and M. Lucic, “Deep generative models for distribution-preserving lossy compression,” in Advances in Neural Information Processing Systems . Curran Associates, Inc., 2018, pp. 5929–5940
2018
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T. Wang, M. Liu, J. Zhu, A. Tao, J. Kautz, and B. Catanzaro, “High-resolution image synthesis and semantic manipulation with conditional gans,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018
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E. Agustsson, M. Tschannen, F. Mentzer, R. Timofte, and L. V. Gool, “Generative adversarial networks for extreme learned image compression,” in The IEEE International Conference on Computer Vision (ICCV) , October 2019
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F. Bellard, “Bpg image format.” [Online]. Available: https://bellard.org/bpg/
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Y. Blau and T. Michaeli, “Rethinking lossy compression: The rate-distortion-perception tradeoff,” in International Conference on Machine Learning, ICML , ser. Proceedings of Machine Learning Research, vol. 97. PMLR, 2019, pp. 675–685
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Z. Cheng, H. Sun, M. Takeuchi, and J. Katto, “Learned image compression with discretized gaussian mixture likelihoods and attention modules,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020
2020
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L. Wu, K. Huang, and H. Shen, “A gan-based tunable image compression system,” in IEEE Winter Conference on Applications of Computer Vision (WACV) , 2020, pp. 2323–2331
2020
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J. Lee, D. Kim, Y. Kim, H. Kwon, J. Kim, and T. Lee, “A training method for image compression networks to improve perceptual quality of reconstructions,” in The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , June 2020
2020
Closest in time.