AI RESEARCH
Content-Aware Mamba for Learned Image Compression
arXiv CS.CV
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ArXi:2508.02192v5 Announce Type: replace Recent learned image compression (LIC) leverages Mamba-style state-space models (SSMs) for global receptive fields with linear complexity. However, the standard Mamba adopts content-agnostic, predefined raster (or multi-directional) scans under strict causality. This rigidity hinders its ability to effectively eliminate redundancy between tokens that are content-correlated but spatially distant. We