基 于 全 局 相 关 语 义 重 要 性 的 语 义 压 缩 算 法

A novel semantic compression algorithm was proposed, aiming to address the inadequacies of traditional compression methods in retaining deep semantic information. The global correlated semantic importance (GCSI) was used as a semantic importance measurement parameter, and the semantic task relevance and semantic intrinsic relevance metrics were integrated to assess the importance of semantic features and achieve effective semantic compression. Experimental results show that the compression performance of the proposed algorithm is improved by more than 30% compared with traditional methods under different channel conditions, and the classification accuracy of the proposed algorithm is enhanced by more than 10% in low bandwidth and low signal-to-noise ratio (SNR) environments. Under the same bandwidth and performance requirements, the proposed algorithm exhibits superior noise stability compared to existing semantic compression methods based on semantic task relevance. The proposed algorithm significantly alleviates network transmission pressure, enhances task performance, and can meet the increasing data transmission requirements.