A NEW MULTIHYPOTHESIS PREDICTION SCHEME FOR COMPRESSED VIDEO SENSING RECONSTRUCTION

For multihypothesis-based compressed video sensing schemes, the low accuracy of weight prediction and degradation of recovery quality for high-motion videos are open challenges. To solve this problem, this paper proposes a new multihy-pothesis prediction scheme. To efficiently get high-quality hypotheses, a new hypotheses acquiring method is proposed by building the search window based on the temporal and spatial correlation. To improve the accuracy of weight prediction, a residual transforming preprocessing for weight prediction is proposed. By converting the original hypotheses to residual hypotheses, the influence of quality fluctuation of hypotheses on the recovery quality is suppressed effectively. The sparsity and accuracy of the prediction model are improved efficiently. Simulation results show that a significant improvement in recovery quality is obtained in the proposed scheme compared to the state-of-the-art systems.