AI RESEARCH
A Compact Hybrid Convolution--Frequency State Space Network for Learned Image Compression
arXiv CS.CV
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ArXi:2511.20151v2 Announce Type: replace Learned image compression (LIC) has recently benefited from Transformer- and state space models (SSM)- based backbones for modeling long-range dependencies. However, the former typically incurs quadratic complexity, whereas the latter often disrupts neighborhood continuity by flattening 2D features into 1D sequences. To address these issues, we propose a compact Hybrid Convolution and Frequency State Space Network (HCFSSNet) for