A New Multihypothesis-Based Compressed Video Sensing Reconstruction System

Multihypothesis-based compressed video sensing scheme attracts wide attention in the research of resource-constrained video application scenarios. However, high-accuracy weight prediction of hypotheses is always challenging especially for the high-motion sequences. To solve this problem, this paper proposes a novel multihypothesis-based distributed compressed video sensing (NMH-DCVS) system. The new multihypothesis system contains two components: hypotheses acquisition, and weight prediction. First, to acquire more high-quality hypotheses, a new hypotheses acquisition scheme is proposed by constructing the search window based on the temporal, and spatial correlation of the image blocks, respectively. The optimal matching block can be quickly determined. Second, to improve the accuracy of the multihypothesis weight prediction, a new residual transforming preprocessing-based weight prediction algorithm is proposed by transforming the original hypothesis set to residual hypothesis set. The influence of the quality fluctuation of the hypotheses on prediction accuracy is effectively suppressed. Moreover, the improved hypotheses further improve the sparsity of the residual hypothesis set, leading to the additional improvement of the accuracy of the proposed residual-based weight prediction algorithm. Experiment results show that compared with the state-of-the-art methods reported in the literature, the proposed new multihypothesis system significantly improves the decoding performance both in objective, and subjective quality.

Automated summary

This paper proposes a novel multihypothesis-based distributed compressed video sensing system that improves system accuracy and performance through new acquisition and weight prediction methods.