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In this paper, we propose a new framework for compressive video sensing (CVS)that exploits the inherent spatial and temporal redundancies of a videosequence, effectively. The proposed method splits the video sequence into thekey and non-key frames followed by dividing each frame into smallnon-overlapping blocks of equal sizes. At the decoder side, the key frames arereconstructed using adaptively learned sparsifying (ALS) basis via $\ell_0$minimization, in order to exploit the spatial redundancy. Also, theeffectiveness of three well-known dictionary learning algorithms isinvestigated in our method. For recovery of the non-key frames, a prediction ofthe current frame is initialized, by using the previous reconstructed frame, inorder to exploit the temporal redundancy. The prediction is employed in aproper optimization problem to recover the current non-key frame. To compareour experimental results with the results of some other methods, we employ peaksignal to noise ratio (PSNR) and structural similarity (SSIM) index as thequality assessor. The numerical results show the adequacy of our proposedmethod in CVS.

Compressive Video Sensing via Dictionary Learning and Forward Prediction