IEICE TRANSACTIONS on Information
Perceptual Distributed Compressive Video Sensing via Reweighted Sampling and Rate-Distortion Optimized Measurements Allocation
Summary :
Distributed compressive video sensing (DCVS) is a new paradigm for low-complexity video compression. To achieve the highest possible perceptual coding performance under the measurements budget constraint, we propose a perceptual optimized DCVS codec by jointly exploiting the reweighted sampling and rate-distortion optimized measurements allocation technologies. A visual saliency modulated just-noticeable distortion (VS-JND) profile is first developed based on the side information (SI) at the decoder side. Then the estimated correlation noise (CN) between each non-key frame and its SI is suppressed by the VS-JND. Subsequently, the suppressed CN is utilized to determine the weighting matrix for the reweighted sampling as well as to design a perceptual rate-distortion optimization model to calculate the optimal measurements allocation for each non-key frame. Experimental results indicate that the proposed DCVS codec outperforms the other existing DCVS codecs in term of both the objective and subjective performance.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.4 pp.918-922
- Publication Date
- 2017/04/01
- Publicized
- 2017/01/06
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016EDL8179
- Type of Manuscript
- LETTER
- Category
- Image Processing and Video Processing
Authors
Jin XU
UESTC
Yan ZHANG
Guiyang University
Zhizhong FU
UESTC
Ning ZHOU
UESTC
Keyword
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Jin XU, Yan ZHANG, Zhizhong FU, Ning ZHOU, "Perceptual Distributed Compressive Video Sensing via Reweighted Sampling and Rate-Distortion Optimized Measurements Allocation" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 4, pp. 918-922, April 2017, doi: 10.1587/transinf.2016EDL8179.
Abstract: Distributed compressive video sensing (DCVS) is a new paradigm for low-complexity video compression. To achieve the highest possible perceptual coding performance under the measurements budget constraint, we propose a perceptual optimized DCVS codec by jointly exploiting the reweighted sampling and rate-distortion optimized measurements allocation technologies. A visual saliency modulated just-noticeable distortion (VS-JND) profile is first developed based on the side information (SI) at the decoder side. Then the estimated correlation noise (CN) between each non-key frame and its SI is suppressed by the VS-JND. Subsequently, the suppressed CN is utilized to determine the weighting matrix for the reweighted sampling as well as to design a perceptual rate-distortion optimization model to calculate the optimal measurements allocation for each non-key frame. Experimental results indicate that the proposed DCVS codec outperforms the other existing DCVS codecs in term of both the objective and subjective performance.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDL8179/_p
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@ARTICLE{e100-d_4_918,
author={Jin XU, Yan ZHANG, Zhizhong FU, Ning ZHOU, },
journal={IEICE TRANSACTIONS on Information},
title={Perceptual Distributed Compressive Video Sensing via Reweighted Sampling and Rate-Distortion Optimized Measurements Allocation},
year={2017},
volume={E100-D},
number={4},
pages={918-922},
abstract={Distributed compressive video sensing (DCVS) is a new paradigm for low-complexity video compression. To achieve the highest possible perceptual coding performance under the measurements budget constraint, we propose a perceptual optimized DCVS codec by jointly exploiting the reweighted sampling and rate-distortion optimized measurements allocation technologies. A visual saliency modulated just-noticeable distortion (VS-JND) profile is first developed based on the side information (SI) at the decoder side. Then the estimated correlation noise (CN) between each non-key frame and its SI is suppressed by the VS-JND. Subsequently, the suppressed CN is utilized to determine the weighting matrix for the reweighted sampling as well as to design a perceptual rate-distortion optimization model to calculate the optimal measurements allocation for each non-key frame. Experimental results indicate that the proposed DCVS codec outperforms the other existing DCVS codecs in term of both the objective and subjective performance.},
keywords={},
doi={10.1587/transinf.2016EDL8179},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Perceptual Distributed Compressive Video Sensing via Reweighted Sampling and Rate-Distortion Optimized Measurements Allocation
T2 - IEICE TRANSACTIONS on Information
SP - 918
EP - 922
AU - Jin XU
AU - Yan ZHANG
AU - Zhizhong FU
AU - Ning ZHOU
PY - 2017
DO - 10.1587/transinf.2016EDL8179
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2017
AB - Distributed compressive video sensing (DCVS) is a new paradigm for low-complexity video compression. To achieve the highest possible perceptual coding performance under the measurements budget constraint, we propose a perceptual optimized DCVS codec by jointly exploiting the reweighted sampling and rate-distortion optimized measurements allocation technologies. A visual saliency modulated just-noticeable distortion (VS-JND) profile is first developed based on the side information (SI) at the decoder side. Then the estimated correlation noise (CN) between each non-key frame and its SI is suppressed by the VS-JND. Subsequently, the suppressed CN is utilized to determine the weighting matrix for the reweighted sampling as well as to design a perceptual rate-distortion optimization model to calculate the optimal measurements allocation for each non-key frame. Experimental results indicate that the proposed DCVS codec outperforms the other existing DCVS codecs in term of both the objective and subjective performance.
ER -
English
