PRISM: Progressive Region-Informed State Modeling for Overfitted Image Compression

Overfitted codecs compress each image by fitting a lightweight decoder and a hierarchical latent tailored to the content. Given the inherent trade-off between model expressiveness and parameter rate, existing methods largely focus on representation design and optimization, leaving explicit modeling of dependencies across the latent hierarchy underexplored. We reveal that maintaining and reusing a shared contextual state across latent grids can improve both entropy modeling and image synthesis. Motivated by this, we introduce **P**rogressive **R**egion-**I**nformed **S**tate **M**odeling (PRISM) for overfitted image compression, which progressively encapsulates cross-scale context into a compact state as entropy modeling proceeds through the latent hierarchy. Gated, region-informed updates adapt the state to spatially varying dependencies while selectively retaining information from previously decoded grids for subsequent probability estimation. The accumulated state is further supplied to the synthesis network, where the retained context complements the latent representations for image reconstruction. Experiments on Kodak and CLIC2020 demonstrate state-of-the-art rate-distortion (RD) performance among overfitted codecs, with BD-rate reductions of 9.56% and 20.35% over VTM-19.1, respectively, approaching strong codecs such as LIC-HPCM at substantially lower decoding complexity. These gains extend across diverse overfitted codecs, highlighting the potential of PRISM as a general paradigm for improving both RD performance and convergence.