Idempotence and Perceptual Image Compression

Published: 16 Jan 2024, Last Modified: 08 Mar 2024ICLR 2024 spotlightEveryoneRevisionsBibTeX
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Keywords: perceptual image compression, neural image compression
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TL;DR: Indempotence constraint inversion of unconditional generative model achieve perceptual image compression.
Abstract: Idempotence is the stability of image codec to re-compression. At the first glance, it is unrelated to perceptual image compression. However, we find that theoretically: 1) Conditional generative model-based perceptual codec satisfies idempotence; 2) Unconditional generative model with idempotence constraint is equivalent to conditional generative codec. Based on this newfound equivalence, we propose a new paradigm of perceptual image codec by inverting unconditional generative model with idempotence constraints. Our codec is theoretically equivalent to conditional generative codec, and it does not require training new models. Instead, it only requires a pre-trained mean-square-error codec and unconditional generative model. Empirically, we show that our proposed approach outperforms state-of-the-art methods such as HiFiC and ILLM, in terms of Fréchet Inception Distance (FID). The source code is provided in https://github.com/tongdaxu/Idempotence-and-Perceptual-Image-Compression.
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Primary Area: generative models
Submission Number: 844
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