Low-Complexity Video Compression and Compressive Sensing

Compressive sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. To reduce video-encoder complexity, we present a CS-based video compression scheme. Modern video-encoder complexity arises mainly from the transform-coding and motion-estimation blocks. In our proposed scheme, we eliminate these blocks from the encoder, which achieves compression by merely taking a few linear measurements of each image in a video sequence. To guarantee stable reconstruction of the video sequence from only a few measurements, the decoder must effectively exploit the inherent spatial and temporal redundancies in a video sequence. To leverage these redundancies, we consider a motion-adaptive linear dynamical model for videos. Recovery process involves solving an l(1)-regularized optimization problem, which iteratively updates estimates for the video frames and motion within adjacent frames. To evaluate the performance of our proposed scheme we performed experiments on various standard test sequences.