arXiv cs.AI· Xiwen Zhang, Yubo Peng, Luping Xiang, Kun Yang·· 10 小时前AI 评分22
Ti-SemCom：基于 token 重要性感知的语义通信实现鲁棒视频传输
Token Importance-Aware Semantic Communication Enabled Robust Video Transmission
AI 导读
研究者提出 token 重要性感知语义通信框架 Ti-SemCom，通过联合提取物体与运动信息识别通信相关区域，并按重要性将语义 token 分层，结合可用带宽动态选择传输。接收端对未传输区域复用近期重建参考帧以减少冗余传输。在 CAVIAR 数据集上，该方法在保持有竞争力重建质量的同时将通信开销降低 88.3%。
正文 · 原文
Abstract:Video applications are increasingly prevalent, yet their high data volume and temporal dependency make robust wireless transmission challenging under limited communication resources. To address this issue, we propose a token importance-aware semantic communication (Ti-SemCom) framework for bandwidth-efficient video transmission. Specifically, object and motion information is jointly extracted to identify communication-relevant regions, which are hierarchically associated with semantic tokens according to their importance. The transmitted tokens are then dynamically selected based on both token importance and the available bandwidth, prioritizing more informative video content under resource constraints. At the receiver, untransmitted regions reuse recently reconstructed references to further reduce redundant transmission. Experiments on the CAVIAR dataset show that Ti-SemCom can achieve an 88.3\% reduction in communication cost while maintaining competitive reconstruction quality.
| Subjects: | Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Multimedia (cs.MM) |
| Cite as: | arXiv:2611.12928 [eess.IV] |
| (or arXiv:2611.12928v1 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.12928 arXiv-issued DOI via DataCite |
Submission history
From: Yubo Peng [view email]
[v1]
Tue, 3 Nov 2026 10:46:02 UTC (1,115 KB)
来源：arXiv cs.AI · arxiv.org