让图像压缩模型学会根据内容动态调整处理顺序，提升压缩效率。
Content-Aware Mamba for Learned Image Compression
- 用内容自适应的令牌重排替代固定扫描顺序，优先处理语义相似的区域。
- 引入样本专属全局先验，突破严格因果限制，更好捕捉远距离冗余。
- 在多个数据集上显著优于现有方法，适合追求高效图像压缩的研究者。
近期学习型图像压缩（LIC）采用类似Mamba的状态空间模型（SSMs），以线性复杂度实现全局感受野。然而，标准Mamba依赖内容无关的预定义光栅（或双向）扫描，且受严格因果性约束，难以有效消除语义相关但空间分散的令牌间冗余。本文提出内容感知Mamba（CAM），通过两项创新克服上述局限：首先，以内容自适应的令牌重排策略取代刚性扫描，优先关联语义相似的令牌，无论其位置远近；其次，通过向状态空间模型注入样本特定的全局先验，缓解严格因果性带来的限制，无需多方向扫描即可实现全局交互。这些改进使CAM能更有效地捕获全局冗余，同时保持计算高效。基于CAM的图像压缩模型（CMIC）在Kodak、Tecnick和CLIC数据集上，分别比VTM-21.0降低15.91%、21.34%和17.58%的BD-rate，达到当前最优率失真性能。代码将开源于https://github.com/UnoC-727/CMIC。
原文摘要 · Abstract (English)
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.
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