2024/10/23 by Jiarun Ding, Ding, Jiarun, Peiwen Jiang +4 · 2 citations
Computer Science · #Advanced Data Compression Techniques #Advanced Image and Video Retrieval Techniques #FOS: Electrical engineering #Image Retrieval and Classification Techniques #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2410.17536
openalex publication_date 2024/10/23 · openalex created_date 2024/11/13 · openalex updated_date 2026/07/28
The rapid development of artificial intelligence has significantly advanced semantic communications, particularly in wireless image transmission. However, most existing approaches struggle to precisely distinguish and prioritize image content, and they do not sufficiently incorporate semantic priorities into system design. In this study, we propose an adaptive wireless image semantic transmission scheme called ASCViT-JSCC, which utilizes vision transformer-based joint source-channel coding (JSCC). This scheme prioritizes different image regions based on their importance, identified through object and feature point detection. Unimportant background sections are masked, enabling them to be recovered at the receiver, while the freed resources are allocated to enhance object protection via the JSCC network. We also integrate quantization modules to enable compatibility with quadrature amplitude modulation, commonly used in modern wireless communications. To address frequency-selective fading channels, we introduce CSIPA-Net, which allocates power based on channel information, further improving performance. Notably, we conduct over-the-air testing on a prototype platform composed of a software-defined radio and embedded graphics processing unit systems, validating our methods. Both simulations and real-world measurements demonstrate that ASCViT-JSCC effectively prioritizes object protection according to channel conditions, significantly enhancing image reconstruction quality, especially in challenging channel environments.