A novel block-based algorithm for compressive video sensing

Proposed a block-based compressive video sensing algorithm, which allows to pursue video acquisition and video compressed coding synchronously. To explore the temporal redundancy of the video, different sensing strategies were used between reference frames and non-reference frames: for reference frames, the frames were divided to little patches and employed regular compressive sensing to every patch; while for the rest ones, first the frams were divided into blocks in the same size and then compared the blocks with the corresponding block in reference frame, pursue different sensing method according to the results. The frame quality is better because non-reference frame observation could feedback the reference frames. Meanwhile, the sampling rate changes adaptively according to the texture complication. The experimental results with 20% less samples than other methods indicate that the algorithm is more suitable to human eyes and also gets higher PSNR.