  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. Code is available
at https://github.com/QinSY123/2024-MambaVC.
