@inproceedings{414e4fe0e8f64e168ffdf7b7b300241d,
title = "Deep Joint Source Channel Coding via Attention for Wireless Image Transmission",
abstract = "In digital communication, efficiently transmitting image and video data through constrained channels remains challenging nowadays. Traditional methods using separate source and channel coding often fail in dynamic environments. In this paper, we introduce a novel deep learning based (DL) attention joint source channel coding (AttenJSCC) approach, which enhances robustness and efficiency in wireless image transmissions. By integrating source and channel coding into a unified framework and incorporating our Enhanced Attention Feature (EAF) modules and the ECA attention mechanism, our method outperforms some of the existing JSCC techniques, especially in low SNR conditions. Our framework not only overcomes the limitations of current technologies but also reduces the storage and computational needs on edge devices, facilitating more efficient real time communication.",
keywords = "Attention Mechanisms, Deep Learning, JSCC, Wireless Image Transmission",
author = "Haoze Chang and Lin Ma and Xuedong Wang",
note = "Publisher Copyright: {\textcopyright} ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.; 14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024 ; Conference date: 23-08-2024 Through 25-08-2024",
year = "2025",
doi = "10.1007/978-3-031-86196-3\_27",
language = "英语",
isbn = "9783031861956",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "312--323",
editor = "Hsiao-Hwa Chen and Weixiao Meng",
booktitle = "Wireless and Satellite Systems - 14th EAI International Conference, WiSATS 2024, Proceedings",
address = "德国",
}