用视觉变压器动态压缩图像，按语义重要性分配带宽。
Efficient Semantic Communication Through Transformer-Aided Compression
- 根据图像语义内容差异，动态调整不同区域的压缩率。
- 在TinyImageNet上，重建质量与语义准确率均优于固定压缩方案。
- 适合低带宽环境下需保留关键信息的图像传输任务。
Transformer因其注意力机制能聚焦复杂数据中的关键元素，适用于无线通信系统中的时变信道。本文提出一种面向信道的自适应语义通信框架，基于视觉Transformer将图像块的注意力掩码视为语义重要性的度量，动态划分需以不同速率压缩的图像区域，压缩率随瞬时信道带宽变化。该方法通过自适应编码分辨率提升通信效率，确保在严苛条件下仍保留关键信息。我们在TinyImageNet数据集上评估了该自适应传输框架，同时测量重建质量与分类准确率。结果表明，本方法在保持高语义保真度的同时优化了带宽使用，为有限带宽条件下的多分辨率数据传输提供了有效解决方案。
原文摘要 · Abstract (English)
Transformers, known for their attention mechanisms, have proven highly effective in focusing on critical elements within complex data. This feature can effectively be used to address the time-varying channels in wireless communication systems. In this work, we introduce a channel-aware adaptive framework for semantic communication, where different regions of the image are encoded and compressed based on their semantic content. By employing vision transformers, we interpret the attention mask as a measure of the semantic contents of the patches and dynamically categorize the patches to be compressed at various rates as a function of the instantaneous channel bandwidth. Our method enhances communication efficiency by adapting the encoding resolution to the content's relevance, ensuring that even in highly constrained environments, critical information is preserved. We evaluate the proposed adaptive transmission framework using the TinyImageNet dataset, measuring both reconstruction quality and accuracy. The results demonstrate that our approach maintains high semantic fidelity while optimizing bandwidth, providing an effective solution for transmitting multi-resolution data in limited bandwidth conditions.
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