标题： 基于场景图的概率语义通信用于图像传输 显示英文标题

标题： Scene Graph-Aided Probabilistic Semantic Communication for Image Transmission

摘要： 语义通信强调意义的传输，而不是原始符号。 它为缓解网络拥塞和提高传输效率提供了一个有前景的解决方案。 在本文中，我们提出了一种无线图像通信框架，该框架采用概率图作为分布式用户之间的共享语义知识库。 高级图像语义通过场景图表示，并设计了一个两阶段的压缩算法，以基于学习到的条件和共现概率去除可预测的组件。 在发送端，该算法过滤冗余的关系和实体对，而在接收端，语义恢复利用相同的概率图来重建省略的信息。 为进一步研究，我们也提出了一种多轮语义压缩算法，并对其理论性能进行了分析。 仿真结果表明，我们的语义感知方案实现了优越的传输吞吐量和满意的语义对齐，验证了利用高级语义进行图像通信的有效性。 显示英文摘要

摘要： Semantic communication emphasizes the transmission of meaning rather than raw symbols. It offers a promising solution to alleviate network congestion and improve transmission efficiency. In this paper, we propose a wireless image communication framework that employs probability graphs as shared semantic knowledge base among distributed users. High-level image semantics are represented via scene graphs, and a two-stage compression algorithm is devised to remove predictable components based on learned conditional and co-occurrence probabilities. At the transmitter, the algorithm filters redundant relations and entity pairs, while at the receiver, semantic recovery leverages the same probability graphs to reconstruct omitted information. For further research, we also put forward a multi-round semantic compression algorithm with its theoretical performance analysis. Simulation results demonstrate that our semantic-aware scheme achieves superior transmission throughput and satiable semantic alignment, validating the efficacy of leveraging high-level semantics for image communication.