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Diffusion probabilistic models have recently achieved remarkable success ingenerating high quality image and video data. In this work, we build on thisclass of generative models and introduce a method for lossy compression of highresolution images. The resulting codec, which we call DIffuson-based ResidualAugmentation Codec (DIRAC), is the first neural codec to allow smooth traversalof the rate-distortion-perception tradeoff at test time, while obtainingcompetitive performance with GAN-based methods in perceptual quality.Furthermore, while sampling from diffusion probabilistic models is notoriouslyexpensive, we show that in the compression setting the number of steps can bedrastically reduced.

A Residual Diffusion Model for High Perceptual Quality Codec Augmentation