Multi-Hypothesis Prediction Scheme Based on the Joint Sparsity Model

Can CHEN  Chao ZHOU  Jian LIU  Dengyin ZHANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.11   pp.2214-2220
Publication Date: 2019/11/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDP7133
Type of Manuscript: PAPER
Category: Image Processing and Video Processing
distributed compressive video sensing (DCVS),  multi-hypothesis (MH) reconstruction,  joint sparsity model (JSM),  wireless video sensor networks (WVSNs),  

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Distributed compressive video sensing (DCVS) has received considerable attention due to its potential in source-limited communication, e.g., wireless video sensor networks (WVSNs). Multi-hypothesis (MH) prediction, which treats the target block as a linear combination of hypotheses, is a state-of-the-art technique in DCVS. The common approach is under the supposition that blocks that are dissimilar from the target block are given lower weights than blocks that are more similar. This assumption can yield acceptable reconstruction quality, but it is not suitable for scenarios with more details. In this paper, based on the joint sparsity model (JSM), the authors present a Tikhonov-regularized MH prediction scheme in which the most similar block provides the similar common portion and the others blocks provide respective unique portions, differing from the common supposition. Specifically, a new scheme for generating hypotheses and a Euclidean distance-based metric for the regularized term are proposed. Compared with several state-of-the-art algorithms, the authors show the effectiveness of the proposed scheme when there are a limited number of hypotheses.