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High-Fidelity Generative Image Compression
[article]
2020
arXiv
pre-print
We extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system. In particular, we investigate normalization layers, generator and discriminator architectures, training strategies, as well as perceptual losses. In contrast to previous work, i) we obtain visually pleasing reconstructions that are perceptually similar to the input, ii) we operate in a broad range of bitrates, and iii) our approach can be
arXiv:2006.09965v3
fatcat:i7d6cpbeobfidf4jwqo2rt5fcm