Neural Image Compression with a Diffusion-based Decoder

Noor Fathima Khanum Mohamed Ghouse, Jens Petersen, Auke J. Wiggers, Tianlin Xu, Guillaume Sautiere

Keywords: neural, image, lossy, compression, diffusion, gan, perceptual

TL;DR: Diffusion-based neural image codec allowing smooth and competitive rate-distortion-perception traversal at test time.

Abstract: 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 \emph{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.

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