Title: Re2IC: Realism-Enhanced Region-Based Implicit Codec with Wavelet–Wasserstein Distortion
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Implicit codec, perceputal compression, image compression, overfitted codec, low-complexity compression

Abstract: Recent advances in perceptual image compression improve quality through powerful probabilistic models but often incur prohibitive decoding costs. In this paper, we propose a low-complexity alternative called **Re**alism-enhanced **Re**gion-based **I**mplicit **C**odec, Re$^2$IC, that models visual perception region-by-region with saliency-guided partitioning.To further boost realism, we introduce wavelet–Wasserstein distortion (WA-WD), which balances fidelity and perception by explicitly controlling frequency characteristics. Together, these components enable fine-grained spatial–spectral optimization, allowing Re$^2$IC to achieve superior rate–perception trade-offs and outperform generative codecs like HiFiC while using less than $1\%$ of their decoding cost. The design accelerates coding convergence, reduces decoding complexity, and produces high-quality reconstructions that preserve both fine textures and natural details. Experiments and user studies confirm that Re$^2$IC sets a new state of the art in rate–perception performance among overfitted codecs. Beyond compression, WA-WD also serves as a standalone, tunable metric, that alights more closely with human preference, achieving the highest Pearson (94.6%) and Spearman (92.3%) correlations.