High-Fidelity Image Compression with Score-based Generative Models
Authors: name: Emiel Hoogeboom, name: Eirikur Agustsson, name: Fabian Mentzer, name: Luca Versari, name: George Toderici, name: Lucas Theis

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