Semantic-Aware Visual Information Transmission With Key Information Extraction Over Wireless Networks
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arXiv
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| Format: | Preprint |
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2025
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| author | Zhu, Chen Liang, Kang Bao, Jianrong Zhao, Zhouxiang Yang, Zhaohui Zhang, Zhaoyang Shikh-Bahaei, Mohammad |
| author_facet | Zhu, Chen Liang, Kang Bao, Jianrong Zhao, Zhouxiang Yang, Zhaohui Zhang, Zhaoyang Shikh-Bahaei, Mohammad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12786 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic-Aware Visual Information Transmission With Key Information Extraction Over Wireless Networks Zhu, Chen Liang, Kang Bao, Jianrong Zhao, Zhouxiang Yang, Zhaohui Zhang, Zhaoyang Shikh-Bahaei, Mohammad Computer Vision and Pattern Recognition 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. |
| title | Semantic-Aware Visual Information Transmission With Key Information Extraction Over Wireless Networks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.12786 |