Content-Aware Mamba for Learned Image Compression
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912969969893376 |
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| author | Chen, Yunuo Lyu, Zezheng He, Bing Hu, Hongwei Wang, Qi Tian, Yuan Song, Li Zhang, Wenjun Lu, Guo |
| author_facet | Chen, Yunuo Lyu, Zezheng He, Bing Hu, Hongwei Wang, Qi Tian, Yuan Song, Li Zhang, Wenjun Lu, Guo |
| contents | 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 will be released at https://github.com/UnoC-727/CMIC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02192 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Content-Aware Mamba for Learned Image Compression Chen, Yunuo Lyu, Zezheng He, Bing Hu, Hongwei Wang, Qi Tian, Yuan Song, Li Zhang, Wenjun Lu, Guo Computer Vision and Pattern Recognition 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 will be released at https://github.com/UnoC-727/CMIC. |
| title | Content-Aware Mamba for Learned Image Compression |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.02192 |