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Multi-Hypothesis Prediction Scheme Based on the Joint Sparsity Model
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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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.11 pp.2214-2220
- Publication Date
- 2019/11/01
- Publicized
- 2019/08/05
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019EDP7133
- Type of Manuscript
- PAPER
- Category
- Image Processing and Video Processing
Authors
Can CHEN
Nanjing University of Posts and Telecommunications
Chao ZHOU
Nanjing University of Posts and Telecommunications
Jian LIU
Nanjing University of Finance and Economics
Dengyin ZHANG
Nanjing University of Posts and Telecommunications
Keyword
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Can CHEN, Chao ZHOU, Jian LIU, Dengyin ZHANG, "Multi-Hypothesis Prediction Scheme Based on the Joint Sparsity Model" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 11, pp. 2214-2220, November 2019, doi: 10.1587/transinf.2019EDP7133.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7133/_p
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@ARTICLE{e102-d_11_2214,
author={Can CHEN, Chao ZHOU, Jian LIU, Dengyin ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Multi-Hypothesis Prediction Scheme Based on the Joint Sparsity Model},
year={2019},
volume={E102-D},
number={11},
pages={2214-2220},
abstract={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.},
keywords={},
doi={10.1587/transinf.2019EDP7133},
ISSN={1745-1361},
month={November},}
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TY - JOUR
TI - Multi-Hypothesis Prediction Scheme Based on the Joint Sparsity Model
T2 - IEICE TRANSACTIONS on Information
SP - 2214
EP - 2220
AU - Can CHEN
AU - Chao ZHOU
AU - Jian LIU
AU - Dengyin ZHANG
PY - 2019
DO - 10.1587/transinf.2019EDP7133
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2019
AB - 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.
ER -
English
