面向带宽受限视觉通信的信道无关语义压缩
Channel-Agnostic Semantic Compression for Bandwidth-Limited Visual Communication
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中文总结 AI 辅助
针对带宽受限视觉通信的信道无关需求，提出RQ-NAC语义压缩框架，结合残差量化与n元语法驱动的算术编码，实现超600倍压缩且保持高感知质量，具备高效灵活可靠的语义传输能力。
中文摘要 AI 辅助
带宽受限的视觉通信系统需要在动态无线条件下高效传输高维数据。现有方法要么依赖联合信源信道编码，该方法将表示学习与信道模型紧密耦合，在不同环境下缺乏灵活性；要么采用生成式重建技术，可能引入语义不一致的输出。本文提出RQ-NAC，一种面向视觉通信的信道无关语义压缩框架。该方法利用残差量化生成可扩展的离散语义表示，实现对率失真权衡的细粒度、可预测控制；为进一步提升压缩效率，集成了n元语法驱动的算术编码模块，该模块利用潜在索引间的上下文依赖关系进行无损熵编码。大量实验表明，与未压缩的视觉数据相比，RQ-NAC实现了超过600倍的压缩，同时保持了高感知质量，结果表明该方法可在带宽受限条件下实现高效、灵活且可靠的语义传输。
英文摘要
Bandwidth-limited visual communication systems require efficient transmission of high-dimensional data under dynamic wireless conditions. Existing approaches either rely on joint source-channel coding, which tightly couples representation learning with channel models and lacks flexibility across varying environments, or adopt generative reconstruction techniques that may introduce semantically inconsistent outputs. In this paper, we propose RQ-NAC, a channel-agnostic semantic compression framework for visual communication. The proposed method leverages residual quantization to produce scalable discrete semantic representations, enabling fine-grained and predictable control over the rate-distortion tradeoff. To further enhance compression efficiency, we integrate an n-gram-driven arithmetic coding module that exploits contextual dependencies among latent indices for lossless entropy coding. Extensive experiments demonstrate that RQ-NAC achieves over 600$\times$ compression relative to uncompressed visual data while preserving high perceptual quality. The results show that our approach enables efficient, flexible, and reliable semantic transmission under bandwidth-constrained conditions.
