Lossy Image Compression with Foundation Diffusion Models

arXiv:2404.08580v1 Announce Type: cross
Abstract: Incorporating diffusion models in the image compression domain has the potential to produce realistic and detailed reconstructions, especially at extremely low bitrates. Previous methods focus on using diffusion models as expressive decoders robust to quantization errors in the conditioning signals, yet achieving competitive results in this manner requires costly training of the diffusion model and long inference times due to the iterative generative process. In this work we formulate the removal of quantization error as …

abstract arxiv compression cs.cv diffusion diffusion models domain eess.iv errors focus foundation image low quantization results robust type