Diffusion probabilistic models have recently achieved remarkable success in generating high quality image and video data. In this work, we build on this class of generative models and introduce a method for lossy compression of high resolution images. The resulting codec, which we call DIffuson-based Residual Augmentation Codec (DIRAC), is the first neural codec to allow smooth traversal of the rate-distortion-perception tradeoff at test time, while obtaining competitive performance with GAN-based methods in perceptual quality. Furthermore, while sampling from diffusion probabilistic models is notoriously expensive, we show that in the compression setting the number of steps can be drastically reduced.
@article{arxiv.2301.05489,
title = {A Residual Diffusion Model for High Perceptual Quality Codec Augmentation},
author = {Noor Fathima Ghouse and Jens Petersen and Auke Wiggers and Tianlin Xu and Guillaume Sautière},
journal= {arXiv preprint arXiv:2301.05489},
year = {2023}
}
Comments
v1: 26 pages, 13 figures v2: corrected typo in first author name in arxiv metadata v3: major paper update to add base codecs and lpips loss