In this paper, we propose a semantic communication approach based on probabilistic graphical model (PGM). The proposed approach involves constructing a PGM from a training dataset, which is then shared as common knowledge between the transmitter and receiver. We evaluate the importance of various semantic features and present a PGM-based compression algorithm designed to eliminate predictable portions of semantic information. Furthermore, we introduce a technique to reconstruct the discarded semantic information at the receiver end, generating approximate results based on the PGM. Simulation results indicate a significant improvement in transmission efficiency over existing methods, while maintaining the quality of the transmitted images.

翻译：本文提出了一种基于概率图模型（PGM）的语义通信方法。该方法通过从训练数据集中构建PGM，并将其作为发射端与接收端之间的共享知识库。我们评估了不同语义特征的重要性，并提出了一种基于PGM的压缩算法，旨在消除语义信息中可预测的部分。此外，我们引入了一种在接收端重构被丢弃语义信息的技术，能够基于PGM生成近似结果。仿真结果表明，该方法在保持传输图像质量的同时，相较于现有方法显著提升了传输效率。