Semantic-Aware Visual Information Transmission With Key Information Extraction Over Wireless Networks
This work addresses the challenge of balancing computational efficiency, robustness, and quality for multimedia services in resource-constrained mobile communications, representing an incremental advancement over existing methods.
The paper tackles the problem of inefficient wireless image transmission in 6G networks by proposing an AI-native deep joint source-channel coding framework that integrates key information extraction and adaptive background synthesis, resulting in significant improvements in peak signal-to-noise ratio compared to traditional methods, especially under low-SNR conditions.
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.