JND-based multi-hypothesis Wyner-Ziv video coding

被引：0
|
作者：
Cheng, Jie [1 ]
Meng, Lili [1 ]
Zhang, Jia [1 ,2 ]
Tan, Yanyan [1 ]
Ren, Yuwei [1 ]
Liu, Li [1 ]
Zhang, Huaxiang [1 ]
机构：
[1] Department of Information Science and Engineering, Shandong Normal University, Jinan, China
[2] Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, United States
来源：
Journal of Network Intelligence | 2020年 / 5卷 / 02期
关键词：
Distributed Video Coding (DVC) plays a significant role in video coding sys-tem. Research communities think highly of it on how to improve the system performance. In this paper; we present a distributed video coding scheme based on just noticeable difference (JND). The JND is a measure of maximum image distortion that the human eye cannot detect due to the inherent characteristics of human vision. Therefore; JND model is applied to remove visual redundancy and reduce coding complexity. Meanwhile; the performance of distributed video coding system depends heavily on the quality of the side information (SI); and better performance can be expected when multiple SIs are utilized. Hence; the multi-hypothesis conditional probability density function (pdf) by Bayesian solution and weighted pdf are applied to the proposed scheme respectively. Experimental results illustrate that the proposed scheme is superior to other existing methods; and the Bayesian solution could outperform weighted pdf methods when better SIs are available. © 2020; Taiwan Ubiquitous Information. All rights reserved;
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页码：44 / 53
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