Two-layer compressive sensing based video encoding and decoding framework for WMSN

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 l1 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.