Nowadays, the need for high-quality image reconstruction and restoration is more and more urgent. However, most image transmission systems may suffer from image quality degradation or transmission interruption in the face of interference such as channel noise and link fading. To solve this problem, a relay communication network for semantic image transmission based on shared feature extraction and hyperprior entropy compression (HEC) is proposed, where the shared feature extraction technology based on Pearson correlation is proposed to eliminate partial shared feature of extracted semantic latent feature. In addition, the HEC technology is used to resist the effect of channel noise and link fading and carried out respectively at the source node and the relay node. Experimental results demonstrate that compared with other recent research methods, the proposed system has lower transmission overhead and higher semantic image transmission performance. Particularly, under the same conditions, the multi-scale structural similarity (MS-SSIM) of this system is superior to the comparison method by approximately 0.2.

翻译：当前，高质量图像重建与恢复的需求日益迫切。然而，大多数图像传输系统在面临信道噪声和链路衰落等干扰时，往往会出现图像质量下降或传输中断的问题。为解决这一问题，本文提出一种基于共享特征提取和超先验熵压缩（HEC）的语义图像传输中继通信网络。其中，基于皮尔逊相关的共享特征提取技术被用于消除所提取的语义潜在特征中的部分共享特征。此外，HEC技术被用于抵抗信道噪声与链路衰落的影响，并在源节点和中继节点分别执行。实验结果表明，与近期其他研究方法相比，本系统具有更低的传输开销和更高的语义图像传输性能。特别地，在相同条件下，本系统的多尺度结构相似性（MS-SSIM）比对比方法高出约0.2。