Unified Image Generation and Compression with Hierarchical Progressive Context Modeling

Learned image compression methods typically transform images into the latent domain and model the probability distributions of latents for efficient entropy coding. Image generation models, particularly latent-space generative frameworks, rely on learned probability distributions for latent sampling. More accurate latent distribution modeling can therefore benefit both tasks. However, existing studies, including current generative image compression methods, have rarely explored this synergistic property. To address this research gap, we propose Uni-HPCM, a unified compression–generation framework that uses shared conditional latent distributions for both entropy coding and sampling. Built on the advanced Hierarchical Progressive Context Modeling (HPCM) model, Uni-HPCM is deliberately trained to jointly optimize the rate–distortion cost and perceptual quality. After training, the single unified model supports three versatile paradigms: reconstructive compression via full main-latent coding, pure image generation via full latent sampling, and generative compression across a spectrum of coding–sampling combinations. Experiments demonstrate competitive compression performance, favorable generation quality, and promising results in generative image compression. In addition, a larger model enhances both rate–distortion performance and generation quality, highlighting their potential synergy.