Learned visual compression is an important and active task in multimedia. Existing approaches have explored various CNN- and Transformer-based designs to model content distribution and eliminate redundancy, where balancing efficacy (i.e., rate-distortion trade-off) and efficiency remains a challenge. Recently, state-space models (SSMs) have shown promise due to their long-range modeling capacity and efficiency. Inspired by this, we take the first step to explore SSMs for visual compression. We introduce MambaVC, a simple, strong and efficient compression network based on SSM. MambaVC develops a visual state space (VSS) block with a 2D selective scanning (2DSS) module as the nonlinear activation function after each downsampling, which helps to capture informative global contexts and enhances compression. On compression benchmark datasets, MambaVC achieves superior rate-distortion performance with lower computational and memory overheads. Specifically, it outperforms CNN and Transformer variants by 9.3% and 15.6% on Kodak, respectively, while reducing computation by 42% and 24%, and saving 12% and 71% of memory. MambaVC shows even greater improvements with high-resolution images, highlighting its potential and scalability in real-world applications. We also provide a comprehensive comparison of different network designs, underscoring MambaVC's advantages.

翻译：学习型视觉压缩是多媒体领域一项重要且活跃的任务。现有方法已探索了多种基于CNN和Transformer的设计来建模内容分布并消除冗余，但平衡效能（即率失真权衡）与效率仍具挑战。近年来，状态空间模型因其长程建模能力和高效性展现出潜力。受此启发，我们率先探索将SSM用于视觉压缩。我们提出了MambaVC，一个基于SSM的简单、强大且高效的压缩网络。MambaVC开发了视觉状态空间块，其包含一个二维选择性扫描模块作为每次下采样后的非线性激活函数，这有助于捕获信息丰富的全局上下文并增强压缩性能。在压缩基准数据集上，MambaVC以更低的计算和内存开销实现了优异的率失真性能。具体而言，在Kodak数据集上，其性能分别超过CNN和Transformer变体9.3%和15.6%，同时计算量减少42%和24%，内存占用节省12%和71%。MambaVC在高分辨率图像上表现出更大的改进，凸显了其在实际应用中的潜力和可扩展性。我们还提供了不同网络设计的全面比较，进一步印证了MambaVC的优势。