Iterative Reweighted Tikhonov-Regularized Multihypothesis Prediction Scheme for Distributed Compressive Video Sensing

Abstract

Distributed compressive video sensing (DCVS) has great potential for signal acquisition and processing in source-limited communication, e.g., wireless video sensors networks, because it shifts complicated motion estimation and motion compensation from the encoder to the decoder. Known as a state-of-the-art technique in DCVS, multihypothesis (MH) prediction is widely used because of its acceptable performance and low computational complexity. However, this technique is restricted by inaccurate regularizations, which can cause susceptibility to inaccurate hypotheses. In this paper, we present an iterative reweighted Tikhonov-regularized scheme for MH prediction reconstruction. Specifically, to enhance robustness, this scheme proposes a reweighted Tikhonov regularization that synthetically considers three factors that affect the MH prediction performance—accuracy of the hypothesis set, number of hypotheses, and accuracy of regularizations—by utilizing the influence of each hypothesis. Furthermore, to avoid over-iteration in iterative MH prediction reconstruction, we propose a Bhattacharyya coefficient-based stopping criterion for use in the recovery of non-key frames, in which we exploit the similarity to an adjacent key frame rather than a previous iteration result. The simulation results show that the proposed scheme outperforms the state-of-the-art MH methods in terms of robustness to inaccurate hypotheses when there are a limited number of hypotheses.