title: High-Fidelity Image Compression with Score-based Generative Models
summary: 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.
paper_authors._id: 6475745dd815855e4ef9a16e
paper_authors.name: Emiel Hoogeboom
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-10-28T19:59:48.563Z
paper_authors._id: 671feaa3e269c38584cb065f
paper_authors.avatarUrl: /avatars/d7bc35ab98b208ef1d009aa9b629682a.svg
paper_authors.fullname: Emiel Hoogeboom
paper_authors.type: user
paper_authors.user: Emiel2
paper_authors._id: 6475745dd815855e4ef9a16f
paper_authors.name: Eirikur Agustsson
paper_authors._id: 6475745dd815855e4ef9a170
paper_authors.name: Fabian Mentzer
paper_authors._id: 6475745dd815855e4ef9a171
paper_authors.name: Luca Versari
paper_authors._id: 6475745dd815855e4ef9a172
paper_authors.name: George Toderici
paper_authors._id: 6475745dd815855e4ef9a173
paper_authors.name: Lucas Theis
paper_title: High-Fidelity Image Compression with Score-based Generative Models
paper_summary: 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.
paper_ai_keywords: diffusion generative models text-to-image generation image compression perceptual quality bit-rate FID score two-stage approach autoencoder MSE score-based decoder design decisions