Block Compressed Sensing Reconstruction of Video Combined with Temporal-spatial Characteristics

To improve the rate-distortion performance of video Compressed Sensing (CS) reconstruction, the temporal-spatial characteristics of video are used to jointly recover the video signal in this paper. At the collection terminal, each block in a single-frame is measured at the fixed sampling rates to advoid excessive complexity. At the reconstruction terminal, two regularization terms are respectively added to the minimum Total Variation (TV) reconstruction model to advance the performance of prediction-residual reconstruction, and the terms are constructed in terms of temporal-spatial Auto-Regressive (AR) model and Multiple Hypothesis (MH) model. In addition, considering that the statistics of video source are dynamically varying in spatial and temporal domain, it is discussed how the five different inter-prediction modes impact on precision and computational complexity of reconstruction. Simulation results show that the proposed algorithms effectively improve the quality of reconstructed video at the cost of the computational complexity , and the improvement of inter-prediction mode enhances reconstruction quality in some extent.