{"title":"A Progressive Approach to Joint Source-Channel Coding for Image Super-Resolution Task in Semantic Communications","authors":"Zhen Huang;Yunjian Jia;Wanli Wen;Liang Liang;Jiping Yan;Nanlan Jiang","doi":"10.1109/LWC.2025.3563231","DOIUrl":null,"url":null,"abstract":"The image super-resolution (SR) task in semantic communication can directly apply the delivered information to the downstream SR task, eliminating complex processing at the receiver and significantly improving communication efficiency. This approach is vital for applications in areas such as telemedicine and satellite communications. Nevertheless, developing semantic communication systems for image SR tasks confronts challenges in creating high-performance joint source-channel coding (JSCC) schemes and mitigating wireless channel interference. In this letter, a progressive refinement attention feature (PRAF) module is proposed for the image SR task in semantic communication. This module effectively extracts deep semantic information from images via a progressive feature extraction strategy and adjusts the semantic information according to the SNRs using an improved channel attention mechanism. Building on PRAF, we custom-design the JSCC scheme for image SR tasks in semantic communications. Simulation results validate the effectiveness of the proposed PRAF module and confirm its superiority over existing deep neural networks (DNNs) based JSCC schemes and traditional separated source channel coding schemes.","PeriodicalId":13343,"journal":{"name":"IEEE Wireless Communications Letters","volume":"14 7","pages":"2099-2103"},"PeriodicalIF":5.5000,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Wireless Communications Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10973232/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The image super-resolution (SR) task in semantic communication can directly apply the delivered information to the downstream SR task, eliminating complex processing at the receiver and significantly improving communication efficiency. This approach is vital for applications in areas such as telemedicine and satellite communications. Nevertheless, developing semantic communication systems for image SR tasks confronts challenges in creating high-performance joint source-channel coding (JSCC) schemes and mitigating wireless channel interference. In this letter, a progressive refinement attention feature (PRAF) module is proposed for the image SR task in semantic communication. This module effectively extracts deep semantic information from images via a progressive feature extraction strategy and adjusts the semantic information according to the SNRs using an improved channel attention mechanism. Building on PRAF, we custom-design the JSCC scheme for image SR tasks in semantic communications. Simulation results validate the effectiveness of the proposed PRAF module and confirm its superiority over existing deep neural networks (DNNs) based JSCC schemes and traditional separated source channel coding schemes.
期刊介绍:
IEEE Wireless Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of wireless communications. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of wireless communication systems.