Advances in Diffusion-Based Generative Compression

Abstract:Popularized by their strong image generation performance, diffusion and related methods for generative modeling have found widespread success in visual media applications. In particular, diffusion methods have enabled new approaches to data compression, where realistic reconstructions can be generated at extremely low bit-rates. This article provides a unifying review of recent diffusion-based methods for generative lossy compression, with a focus on image compression. These methods generally encode the source into an embedding and use a diffusion model to iteratively refine it during decoding, so that the reconstruction approximately follows the true data distribution. The embedding can take various forms and is typically transmitted via an auxiliary entropy model, and recent methods also explore the use of diffusion models themselves for information transmission via channel simulation. We review representative approaches through the lens of rate-distortion-perception theory, highlighting the role of common randomness and connections to inverse problems, and identify open challenges.

13 pages, 1 figure. Accepted for publication in IEEE BITS the Information Theory Magazine. v2: revised text, corrected equations, and expanded references

Image and Video Processing (eess.IV); Information Theory (cs.IT); Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2601.18932 [eess.IV]

(or arXiv:2601.18932v2 [eess.IV] for this version)

https://doi.org/10.1109/MBITS.2026.3725614 DOI(s) linking to related resources