Two-layer compressive sensing based video encoding and decoding framework for WMSN. (1st September 2018)

• Record Type: Journal Article
• Authors: Yang, Yang; Guo, Songtao; Liu, Guiyan; Yang, Yuanyuan
• Is Part Of: Journal of network and computer applications. Volume 117(2018)
• Page Start: 72
• Page End: 85
• Publication Date: 2018-09-01
• Subjects: Compressive sensing -- Video compression -- Sparse basis -- Dictionary learning -- Wireless sensor networks; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2018.05.018
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Acquiring massive data from wireless devices such as video sampling and transmission remains a challenge in the resource-restricted wireless multimedia sensor networks (WMSNs). To address the conflict between massive data processing and limited bandwidth or energy, we propose a two-layer compressive sensing based video encoding and decoding framework, which not only significantly reduces the amount of sampling data, but also transfers computation burden from distributed sensor nodes to powerful sink node. To successfully decode compressed data, at the first layer, we construct the first group sparse basis by exploiting the correlation between frames for each sensor node. At the second layer, we consider the spatial correlation between neighbor nodes. By employing dictionary learning, we obtain the second group sparse basis. The sink node employs both groups of sparse bases to perform l 1 norm minimization problem twice and recover original video for each node. Experiment results show that, our CS based framework has high compression efficiency due to our constructed sparse basis. Compared with existing sparse basis, our sparse basis provides better sparse representation.