Compressive video sensing with limited measurements

Compressive sensing (CS) is an innovative technology, allowing us to capture signals with significantly fewer samples than those required by classical Nyquist theory. We propose a novel adaptive video compressive sensing algorithm to exploit the potential of CS in video acquisition. Each frame is divided into blocks to take advantage of its inhomogeneity. We first classify the blocks into one of three types based on their texture complexity and their temporal difference from neighboring frames based on which we determine the number of required measurements. In the reconstruction process, we use the measurements made for the later frames to assist the recovery of previous ones, thus ensuring improved reconstruction quality even when the number of measurements for each frame is limited. Our experimental results demonstrate that we not only obtain significant visual quality improvement but also achieve at least 2.5 dB gain in peak signal- to-noise ratio compared with the existing video compressive sensing algorithms.