Semantic-Aware Visual Information Transmission With Key Information Extraction Over Wireless Networks
Journal:
arXiv
Published Date:
Jun 15, 2025
Abstract
The advent of 6G networks demands unprecedented levels of intelligence,
adaptability, and efficiency to address challenges such as ultra-high-speed
data transmission, ultra-low latency, and massive connectivity in dynamic
environments. Traditional wireless image transmission frameworks, reliant on
static configurations and isolated source-channel coding, struggle to balance
computational efficiency, robustness, and quality under fluctuating channel
conditions. To bridge this gap, this paper proposes an AI-native deep joint
source-channel coding (JSCC) framework tailored for resource-constrained 6G
networks. Our approach integrates key information extraction and adaptive
background synthesis to enable intelligent, semantic-aware transmission.
Leveraging AI-driven tools, Mediapipe for human pose detection and Rembg for
background removal, the model dynamically isolates foreground features and
matches backgrounds from a pre-trained library, reducing data payloads while
preserving visual fidelity. Experimental results demonstrate significant
improvements in peak signal-to-noise ratio (PSNR) compared with traditional
JSCC method, especially under low-SNR conditions. This approach offers a
practical solution for multimedia services in resource-constrained mobile
communications.
