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.
Compressive sensing is an innovative theory which allows us to sample signals under random projection domain. This technique seeks to minimize the cost of redundant data acquisition. In this paper, we propose a new video acquisition system which samples the video volumes with far fewer measurements than traditional camera. Video is divided into little time-spatial volumes due to diverse scene content change among frame regions. With strict sparsity constraints, adaptive dictionary is trained to obtain best representation for little video volumes. In this scheme, K-means clustering and KSVD learning are applied to selected video patches. Experiments and simulation are conducted to test the performance of the capability and adaptivity of the dictionary. Also, visual and PSNR comparison for video acquisition are provided to demonstrate the power of our system. We show that our approach can effectively reconstruct the original video with as few as 5% measurements without losing spatial or temporal resolution.