Adaptive Multihypothesis Prediction Algorithm for Distributed Compressive Video Sensing

A novel adaptive multihypothesis (MH) prediction algorithm for distributed compressive video sensing (DCVS) is proposed in this paper. In the proposed framework, consistent block-based random measurement for each video frame is adopted at the encoder independently. Meanwhile, a mode decision algorithm is applied in CS-blocks via block-based correlation measurements at the decoder. The inter-frame MH mode is selected for the current block wherein the interframe correlation coefficient value exceeds a predetermined threshold. Otherwise, the intraframe MH mode is worthwhile to be selected. Moreover, the adaptive search window and cross-diamond search algorithms on measurement domain are also incorporated to form the dictionary for MH prediction. Both the temporal and spatial correlations in video signals are exploited to enhance CS recovery to satisfy the best linear combination of hypotheses. The simulation results show that the proposed framework can provide better reconstruction quality than the framework using original MH prediction algorithm, and for sequences with slow motion and relatively simple scene composition, the proposed method shows significant performance gains at low measurement subrate.