Idempotence and Perceptual Image Compression
Tongda Xu Ziran Zhu Dailan He Yanghao Li Lina Guo Yuanyuan Wang Zhe Wang Hongwei Qin Yan Wang Jingjing Liu Ya-Qin Zhang
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
Fr\'echet Inception Distance (FID). The source code is provided in
https://github.com/tongdaxu/Idempotence-and-Perceptual-Image-Compression.