发表机构
American University of Beirut（贝鲁特美国大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出信道自适应区域邻接图载体（CA-RAG），通过图简化与置信传播实现高效语义图像通信，在Cityscapes上以更小载荷和更高语义一致性优于深度联合信源信道编码。
AI 中文摘要
语义图像通信旨在有限信道资源下保留与任务相关的场景结构，但现有载体通常是密集的潜在张量或网格对齐的语义布局，未能显式编码区域级关系。本工作引入一种基于分割的区域邻接图（RAG）载体，称为信道自适应RAG（CA-RAG），用于联合信源信道编码风格的图像通信。节点存储可解释的区域属性，边保留邻接关系，信道自适应图简化（CGS）控制节点预算，语义置信传播在基于扩散的重建前细化带噪图嵌入。在Cityscapes数据集上，一项包含2000张图像的研究表明，信道前RAG有效载荷比压缩的类别索引布局小数倍。在信噪比0至15 dB的加性高斯白噪声下，CA-RAG相比深度联合信源信道编码和相同解码器的布局基线，报告了更高的语义一致性，且感知质量相当。在10 dB时，全预算速率扫描点达到平均交并比（mIoU）=0.329，约使用3.3×10^3个信道使用；而默认自适应CGS设置报告mIoU=0.294，约使用2.6×10^3个信道使用。
英文摘要
Semantic image communication seeks to preserve task-relevant scene structure under limited channel resources, but carriers are often dense latent tensors or grid-aligned semantic layouts that do not explicitly encode region-level relations. This work introduces a segmentation-derived region adjacency graph (RAG) carrier, termed channel-adaptive RAG (CA-RAG), for joint source-channel coding-style image communication. Nodes store interpretable region attributes, edges preserve adjacency, channel-adaptive graph simplification (CGS) controls the node budget, and semantic belief propagation refines noisy graph embeddings before diffusion-based reconstruction. On Cityscapes, pre-channel RAG payloads are several times smaller than compressed class-index layouts in a 2,000-image study. Under additive white Gaussian noise at signal-to-noise ratios from 0 to 15 dB, CA-RAG reports higher semantic consistency than deep joint source-channel coding and a same-decoder layout baseline, with comparable perceptual quality. At 10 dB, the full-budget rate-sweep point reaches mean intersection over union (mIoU) = 0.329 at approximately 3.3 x 10^3 channel uses, while the default adaptive-CGS setting reports mIoU = 0.294 at approximately 2.6 x 10^3 channel uses.
Comments6 pages, 5 figures, 7 tables. Accepted for presentation at IEEE GLOBECOM 2026, Macau. This is the author's accepted version; the final published version will be available via IEEE Xplore
