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- W4313303587 abstract "By optimizing the rate-distortion-realism trade-off, generative compression approaches produce detailed, realistic images, even at low bit rates, instead of the blurry reconstructions produced by rate-distortion optimized models. However, previous methods do not explicitly control how much detail is synthesized, which results in a common criticism of these methods: users might be worried that a misleading reconstruction far from the input image is generated. In this work, we alleviate these concerns by training a decoder that can bridge the two regimes and navigate the distortion-realism trade-off. From a single compressed representation, the receiver can decide to either reconstruct a low mean squared error reconstruction that is close to the input, a realistic reconstruction with high perceptual quality, or anything in between. With our method, we set a new state-of-the-art in distortion-realism, pushing the frontier of achievable distortion-realism pairs, i.e., our method achieves better distortions at high realism and better realism at low distortion than ever before." @default.
- W4313303587 created "2023-01-06" @default.
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- W4313303587 date "2022-12-28" @default.
- W4313303587 modified "2023-09-23" @default.
- W4313303587 title "Multi-Realism Image Compression with a Conditional Generator" @default.
- W4313303587 doi "https://doi.org/10.48550/arxiv.2212.13824" @default.
- W4313303587 hasPublicationYear "2022" @default.
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