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.

一种基于共享特征提取和超先验熵压缩的语义图像传输的中继通信网络，用于解决图像传输系统中的图像质量降低和传输中断的问题。实验结果表明，该系统具有更低的传输开销和更高的语义图像传输性能。