Content-Aware Mamba for Learned Image Compression

• URL: http://arxiv.org/abs/2508.02192v4
• Date: Fri, 26 Sep 2025 09:32:40 GMT
• Title: Content-Aware 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: We introduce ContentAware Mamba (CAM), an SSM that dynamically adapts its processing to the image content.<n>First, it replaces the rigid scan with a content-adaptive token permutation strategy.<n>Second, it overcomes the sequential dependency by injecting sample-specific global priors into the state-space model.
• Score: 33.05776457003562
• 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, 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 introduce Content-Aware Mamba (CAM), an SSM that dynamically adapts its processing to the image content. Specifically, CAM overcomes prior limitations with two novel mechanisms. First, it replaces the rigid scan with a content-adaptive token permutation strategy to prioritize interactions between content-similar tokens regardless of their location. Second, it overcomes the sequential dependency by injecting sample-specific global priors into the state-space model, which effectively mitigates the strict causality without multi-directional scans. These innovations enable CAM to better capture global redundancy while preserving computational efficiency. Our Content-Aware 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% in BD-rate on the Kodak, Tecnick, and CLIC datasets, respectively. Code and checkpoints will be released later.

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