Real-Time In-Network Image Compression via Distributed Dictionary Learning
Real-Time In-Network Image Compression via Distributed Dictionary Learning
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DOI：
10.1109/tmc.2021.3072066
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发表时间：
2023-01
影响因子：
7.9
通讯作者：
Parul Pandey;M. Rahmati;W. Bajwa;D. Pompili
中科院分区：
文献类型：
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作者：
Parul Pandey;M. Rahmati;W. Bajwa;D. Pompili
多摄像机网络在许多监控和监视应用中越来越普遍，并且在具有协作、实时决策能力的分布式系统中引起了广泛关注。虽然网络内数据压缩为摄像机节点带来了显著的能量节约，但使用稀疏逼近和过完备字典的信号表示已被证明优于传统的压缩方法。在这项工作中，设计并实施了一个端到端的实时解决方案，通过利用收集的多媒体数据的空间相关性，在分布式摄像机网络中实现节能和强大的字典学习。传统的分布式字典学习依赖于共识构建算法，该算法涉及与相邻节点通信，直到实现收敛。然而，现有的方法并没有利用相机网络中的空间相关性来提高能源效率。相比之下，本研究采用低计算复杂度度量来量化和利用无线网络中相机节点之间的空间相关性，以实现高效的分布式字典学习和网络内图像压缩。通过在公共数据集上的广泛模拟以及在由树莓派节点组成的测试平台上的实际实验，验证了所提出方法的性能。
Multi-camera networks are increasingly becoming pervasive in many monitoring and surveillance applications, and have attracted much attention in distributed systems with collaborative, real-time decision-making capabilities. While in-network data compression brings significant energy savings in camera nodes, signal representation using sparse approximations and overcomplete dictionaries have been shown to outperform traditional compression methods. In this work, an end-to-end and real-time solution is designed and implemented to enable energy-efficient and robust dictionary learning in distributed camera networks by leveraging the spatial correlation of the collected multimedia data. Traditional distributed dictionary learning relies on consensus-building algorithms, which involve communicating with neighboring nodes until convergence is achieved. Existing methods, however, do not exploit spatial correlations in camera networks for improved energy efficiency. In contrast, low-computational-complexity metrics are employed in this work to quantify and exploit the spatial correlation across camera nodes in a wireless network for efficient distributed dictionary learning and in-network image compression. The performance of the proposed approach is validated through extensive simulations on public datasets as well as via real-world experiments on a testbed composed of Raspberry Pi nodes.