用状态空间模型实现高效图像压缩，兼顾性能与速度。
Efficient Image Compression Using Advanced State Space Models
- 基于状态空间模型设计新型压缩架构，提升效率。
- 相比现有方法，压缩率提升且计算量大幅降低。
- 适合移动端、实时视频等对延迟敏感的应用场景。
Transformer 已推动学习型图像压缩方法发展，超越传统方案。但这类方法常因复杂度高而难以实用。为此，研究者尝试知识蒸馏和轻量化结构以提升效率。本文提出一种基于状态空间模型的图像压缩（SSMIC）架构，平衡性能与计算效率，适用于真实应用场景。实验表明，该模型在保持优异编码质量的同时，显著降低计算复杂度和延迟，相比竞争性学习型压缩方法取得更优的 BD-rate 性能。
原文摘要 · Abstract (English)
Transformers have led to learning-based image compression methods that outperform traditional approaches. However, these methods often suffer from high complexity, limiting their practical application. To address this, various strategies such as knowledge distillation and lightweight architectures have been explored, aiming to enhance efficiency without significantly sacrificing performance. This paper proposes a State Space Model-based Image Compression (SSMIC) architecture. This novel architecture balances performance and computational efficiency, making it suitable for real-world applications. Experimental evaluations confirm the effectiveness of our model in achieving a superior BD-rate while significantly reducing computational complexity and latency compared to competitive learning-based image compression methods.
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