@inproceedings{0a295c59c22f4b2ba2bc7f9562a3a3fb,
title = "Measurement-Correlated Distributed Compressed Video Sensing for High-Efficiency Video Recovery",
abstract = "To efficiently process the massive video data collected by wireless sensor networks, this paper proposes a novel method exploiting measurement correlations based on the Distributed Compressed Video Sensing (DCVS) framework. Under the typical resource-constrained scenario where the resources of the data compression encoder are more limited than those of the data reconstruction decoder, DCVS allocates different sensing rates for different frames to improve overall compression efficiency and conserve transmission bandwidth. The proposed method further enhances video reconstruction quality by effectively utilizing the correlated information in compressed measurement vectors. Additionally, motion-compensated residual reconstruction is integrated into the scheme to improve reconstruction accuracy. Our approach achieves superior performance in recovery quality compared to existing methods by conducting experiments.",
keywords = "DCVS, compressive sensing, measurement correlation, video reconstruction",
author = "Yuanqi Zhao and Xuhui Ding and Jiawen Chen and Jiaxuan Li and Yuanyuan Zhang and Jiabao Zhu",
note = "Publisher Copyright: {\textcopyright} 2026 IEEE.; 3rd IEEE International Conference on Electronics, Communications and Intelligent Science, ECIS 2026 ; Conference date: 22-05-2026 Through 24-05-2026",
year = "2026",
doi = "10.1109/ECIS69634.2026.11604375",
language = "English",
series = "Proceeding: ECIS 2026 - 2026 IEEE 3rd International Conference on Electronics, Communications and Intelligent Science",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceeding",
address = "United States",
}