cs.AI updates on arXiv.org 08月05日
Conquering High Packet-Loss Erasure: MoE Swin Transformer-Based Video Semantic Communication
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本文提出一种基于MoE Swin Transformer的视频语义通信系统,通过3D CNN恢复丢失信息,采用语义级交织和压缩技术,在90%丢包率下实现高保真传输。

arXiv:2508.01205v1 Announce Type: cross Abstract: Semantic communication with joint semantic-channel coding robustly transmits diverse data modalities but faces challenges in mitigating semantic information loss due to packet drops in packet-based systems. Under current protocols, packets with errors are discarded, preventing the receiver from utilizing erroneous semantic data for robust decoding. To address this issue, a packet-loss-resistant MoE Swin Transformer-based Video Semantic Communication (MSTVSC) system is proposed in this paper. Semantic vectors are encoded by MSTVSC and transmitted through upper-layer protocol packetization. To investigate the impact of the packetization, a theoretical analysis of the packetization strategy is provided. To mitigate the semantic loss caused by packet loss, a 3D CNN at the receiver recovers missing information using un-lost semantic data and an packet-loss mask matrix. Semantic-level interleaving is employed to reduce concentrated semantic loss from packet drops. To improve compression, a common-individual decomposition approach is adopted, with downsampling applied to individual information to minimize redundancy. The model is lightweighted for practical deployment. Extensive simulations and comparisons demonstrate strong performance, achieving an MS-SSIM greater than 0.6 and a PSNR exceeding 20 dB at a 90% packet loss rate.

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视频语义通信 抗丢包 Swin Transformer 3D CNN 压缩技术
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