IEICE TRANSACTIONS on Information
Resample-Based Hybrid Multi-Hypothesis Scheme for Distributed Compressive Video Sensing
Summary :
Multi-hypothesis prediction technique, which exploits inter-frame correlation efficiently, is widely used in block-based distributed compressive video sensing. To solve the problem of inaccurate prediction in multi-hypothesis prediction technique at a low sampling rate and enhance the reconstruction quality of non-key frames, we present a resample-based hybrid multi-hypothesis scheme for block-based distributed compressive video sensing. The innovations in this paper include: (1) multi-hypothesis reconstruction based on measurements reorganization (MR-MH) which integrates side information into the original measurements; (2) hybrid multi-hypothesis (H-MH) reconstruction which mixes multiple multi-hypothesis reconstructions adaptively by resampling each reconstruction. Experimental results show that the proposed scheme outperforms the state-of-the-art technique at the same low sampling rate.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.12 pp.3073-3076
- Publication Date
- 2017/12/01
- Publicized
- 2017/09/08
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDL8133
- Type of Manuscript
- LETTER
- Category
- Image Processing and Video Processing
Authors
Can CHEN
Nanjing University of Posts and Telecommunications
Dengyin ZHANG
Nanjing University of Posts and Telecommunications
Jian LIU
Nanjing University of Posts and Telecommunications
Keyword
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Can CHEN, Dengyin ZHANG, Jian LIU, "Resample-Based Hybrid Multi-Hypothesis Scheme for Distributed Compressive Video Sensing" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 12, pp. 3073-3076, December 2017, doi: 10.1587/transinf.2017EDL8133.
Abstract: Multi-hypothesis prediction technique, which exploits inter-frame correlation efficiently, is widely used in block-based distributed compressive video sensing. To solve the problem of inaccurate prediction in multi-hypothesis prediction technique at a low sampling rate and enhance the reconstruction quality of non-key frames, we present a resample-based hybrid multi-hypothesis scheme for block-based distributed compressive video sensing. The innovations in this paper include: (1) multi-hypothesis reconstruction based on measurements reorganization (MR-MH) which integrates side information into the original measurements; (2) hybrid multi-hypothesis (H-MH) reconstruction which mixes multiple multi-hypothesis reconstructions adaptively by resampling each reconstruction. Experimental results show that the proposed scheme outperforms the state-of-the-art technique at the same low sampling rate.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDL8133/_p
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@ARTICLE{e100-d_12_3073,
author={Can CHEN, Dengyin ZHANG, Jian LIU, },
journal={IEICE TRANSACTIONS on Information},
title={Resample-Based Hybrid Multi-Hypothesis Scheme for Distributed Compressive Video Sensing},
year={2017},
volume={E100-D},
number={12},
pages={3073-3076},
abstract={Multi-hypothesis prediction technique, which exploits inter-frame correlation efficiently, is widely used in block-based distributed compressive video sensing. To solve the problem of inaccurate prediction in multi-hypothesis prediction technique at a low sampling rate and enhance the reconstruction quality of non-key frames, we present a resample-based hybrid multi-hypothesis scheme for block-based distributed compressive video sensing. The innovations in this paper include: (1) multi-hypothesis reconstruction based on measurements reorganization (MR-MH) which integrates side information into the original measurements; (2) hybrid multi-hypothesis (H-MH) reconstruction which mixes multiple multi-hypothesis reconstructions adaptively by resampling each reconstruction. Experimental results show that the proposed scheme outperforms the state-of-the-art technique at the same low sampling rate.},
keywords={},
doi={10.1587/transinf.2017EDL8133},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Resample-Based Hybrid Multi-Hypothesis Scheme for Distributed Compressive Video Sensing
T2 - IEICE TRANSACTIONS on Information
SP - 3073
EP - 3076
AU - Can CHEN
AU - Dengyin ZHANG
AU - Jian LIU
PY - 2017
DO - 10.1587/transinf.2017EDL8133
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2017
AB - Multi-hypothesis prediction technique, which exploits inter-frame correlation efficiently, is widely used in block-based distributed compressive video sensing. To solve the problem of inaccurate prediction in multi-hypothesis prediction technique at a low sampling rate and enhance the reconstruction quality of non-key frames, we present a resample-based hybrid multi-hypothesis scheme for block-based distributed compressive video sensing. The innovations in this paper include: (1) multi-hypothesis reconstruction based on measurements reorganization (MR-MH) which integrates side information into the original measurements; (2) hybrid multi-hypothesis (H-MH) reconstruction which mixes multiple multi-hypothesis reconstructions adaptively by resampling each reconstruction. Experimental results show that the proposed scheme outperforms the state-of-the-art technique at the same low sampling rate.
ER -
English
