For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Multi-Hypothesis Prediction Scheme Based on the Joint Sparsity Model
Can CHEN Chao ZHOU Jian LIU Dengyin ZHANG
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2019/11/01
Online ISSN: 1745-1361
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),
Full Text: FreePDF(883.3KB)
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.