Multi-hop Parallel Image Semantic Communication for Distortion Accumulation Mitigation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918309801230336 |
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| author | Xie, Bingyan Park, Jihong Wu, Yongpeng Zhang, Wenjun Quek, Tony |
| author_facet | Xie, Bingyan Park, Jihong Wu, Yongpeng Zhang, Wenjun Quek, Tony |
| contents | Existing semantic communication schemes primarily focus on single-hop scenarios, overlooking the challenges of multi-hop wireless image transmission. As semantic communication is inherently lossy, distortion accumulates over multiple hops, leading to significant performance degradation. To address this, we propose the multi-hop parallel image semantic communication (MHPSC) framework, which introduces a parallel residual compensation link at each hop against distortion accumulation. To minimize the associated transmission bandwidth overhead, a coarse-to-fine residual compression scheme is designed. A deep learning-based residual compressor first condenses the residuals, followed by the adaptive arithmetic coding (AAC) for further compression. A residual distribution estimation module predicts the prior distribution for the AAC to achieve fine compression performances. This approach ensures robust multi-hop image transmission with only a minor increase in transmission bandwidth. Experimental results confirm that MHPSC outperforms both existing semantic communication and traditional separated coding schemes. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_26844 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-hop Parallel Image Semantic Communication for Distortion Accumulation Mitigation Xie, Bingyan Park, Jihong Wu, Yongpeng Zhang, Wenjun Quek, Tony Information Theory Multimedia Image and Video Processing Existing semantic communication schemes primarily focus on single-hop scenarios, overlooking the challenges of multi-hop wireless image transmission. As semantic communication is inherently lossy, distortion accumulates over multiple hops, leading to significant performance degradation. To address this, we propose the multi-hop parallel image semantic communication (MHPSC) framework, which introduces a parallel residual compensation link at each hop against distortion accumulation. To minimize the associated transmission bandwidth overhead, a coarse-to-fine residual compression scheme is designed. A deep learning-based residual compressor first condenses the residuals, followed by the adaptive arithmetic coding (AAC) for further compression. A residual distribution estimation module predicts the prior distribution for the AAC to achieve fine compression performances. This approach ensures robust multi-hop image transmission with only a minor increase in transmission bandwidth. Experimental results confirm that MHPSC outperforms both existing semantic communication and traditional separated coding schemes. |
| title | Multi-hop Parallel Image Semantic Communication for Distortion Accumulation Mitigation |
| topic | Information Theory Multimedia Image and Video Processing |
| url | https://arxiv.org/abs/2510.26844 |