Wireless multimedia sensor networks (WMSNs) have been used for sensitive applications such as video surveillance and monitoring applications. In a WMSN, storage and transmission become complicated phenomena that can be simplified by the use of compressed sensing, which asserts that sparse signals can be reconstructed from very few measurements. In this paper, memory-efficient measurement matrices are proposed for a discrete wavelet transform (DWT)–discrete cosine transform (DCT) hybrid approach based video compressed sensing (VCS). The performance of the framework is evaluated in terms of PSNR, storage complexity, transmission energy and delay. The results show that the proposed matrices yield similar or better PSNR and consume less memory for generating the matrix when compared with a Gaussian matrix. The DWT–DCT based VCS yields better quality and compression when compared with DCT and DWT approaches. The transmission energy is 50% less and the average delay is 52% less when compared to raw frame transmission. 
Video Compressed Sensing framework for Wireless Multimedia Sensor Networks using a combination of multiple matrices