CMIC: Content-Adaptive Mamba for Learned Image Compression

• URL: http://arxiv.org/abs/2508.02192v2
• Date: Tue, 05 Aug 2025 13:36:08 GMT
• Title: CMIC: Content-Adaptive Mamba for Learned Image Compression
• Authors: Yunuo Chen, Zezheng Lyu, Bing He, Hongwei Hu, Qi Wang, Yuan Tian, Li Song, Wenjun Zhang, Guo Lu,
• Abstract summary: Recent Learned image compression (LIC) leverages Mamba-style state-space models (SSMs) for global fields with linear complexity.<n>We introduce Content-Adaptive Mamba (CAM), a dynamic SSM that addresses two critical limitations.<n>CAM employs content-aware token reorganization, clustering and reordering tokens based on content similarity to prioritize proximity in feature space over Euclidean space.
• Score: 28.348742499973493
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent Learned image compression (LIC) leverages Mamba-style state-space models (SSMs) for global receptive fields with linear complexity. However, vanilla Mamba is content-agnostic, relying on fixed and predefined selective scans, which restricts its ability to dynamically and fully exploit content dependencies. We introduce Content-Adaptive Mamba (CAM), a dynamic SSM that addresses two critical limitations. First, it employs content-aware token reorganization, clustering and reordering tokens based on content similarity to prioritize proximity in feature space over Euclidean space. Second, it integrates global priors into SSM via a prompt dictionary, effectively mitigating the strict causality and long-range decay in the token interactions of Mamba. These innovations enable CAM to better capture global dependencies while preserving computational efficiency. Leveraging CAM, our Content-Adaptive Mamba-based LIC model (CMIC) achieves state-of-the-art rate-distortion performance, surpassing VTM-21.0 by -15.91\%, -21.34\%, and -17.58\% BD-rate on Kodak, Tecnick, and CLIC benchmarks, respectively.

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