Lossy Image Compression with Conditional Diffusion Models

Denoising diffusion models have recently marked a milestone in high-quality image generation. One may thus wonder if they are suitable for neural image compression. This paper outlines an end-to-end optimized image compression framework based on a conditional diffusion model, drawing on the transform-coding paradigm. Besides the latent variables inherent to the diffusion process, this paper introduces an additional discrete "content" latent variable to condition the denoising process on. This variable is equipped with a hierarchical prior for entropy coding. The remaining "texture" latent variables characterizing the diffusion process are synthesized (either stochastically or deterministically) at decoding time. We furthermore show that the performance can be tuned toward perceptual metrics of interest. Our extensive experiments involving five datasets and 16 image perceptual quality assessment metrics show that our approach not only compares favorably in terms of rate and perceptual distortion tradeoffs but also shows robust performance under all metrics while other baselines show less consistent behavior.