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Despite the tremendous success of diffusion generative models intext-to-image generation, replicating this success in the domain of imagecompression has proven difficult. In this paper, we demonstrate that diffusioncan significantly improve perceptual quality at a given bit-rate, outperformingstate-of-the-art approaches PO-ELIC and HiFiC as measured by FID score. This isachieved using a simple but theoretically motivated two-stage approachcombining an autoencoder targeting MSE followed by a further score-baseddecoder. However, as we will show, implementation details matter and theoptimal design decisions can differ greatly from typical text-to-image models.

High-Fidelity Image Compression with Score-based Generative Models