A Relay System for Semantic Image Transmission Based on Shared Feature Extraction and Hyperprior Entropy Compression

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, the 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, compared with the element-distance-based scheme, the proposed shared feature extraction method based on Pearson correlation not only exhibits an approximate 0.2 advantage in multiscale structural similarity (MS-SSIM) but also efficiently eliminates the burden caused by the transmission of shared feature indexes.