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A New Progressively Refined Wyner-Ziv Video Coding for Low-Power Human-Centered Telehealth

Yang, Jia, He, Xiaohai, Qing, Linbo, Xiong, Shuhua and Peng, Yonghong (2018) A New Progressively Refined Wyner-Ziv Video Coding for Low-Power Human-Centered Telehealth. IEEE Access, 6. pp. 38315-38325. ISSN 2169-3536

Item Type: Article

Abstract

With the increas of the global aging population, elderly care has become an important social issue around the world. Human-centered telehealth provides more efficient and comfortable health-care services for elderly people through collecting the elderly’s information remotely. Video taken by wearable cameras is one of the most efficient carriers for human-centered telehealth. Whereas wearable cameras are mainly limited in energy supply and computation, the conventional video codecs such as H.26x requiring encoders with powerful processing ability are thus not suitable. Distributed video coding (DVC) based
on the Wyner–Ziv (WZ) coding architecture, namely, WZ video coding, can exploit the source statistics
only at decoders. It thus provides an efficient solution for low-power wearable cameras. Nevertheless, the
compression performance gap between the DVC and the conventional video coding still exists. One of the
main reasons for this weakness is the quality of the side information (SI). As the estimation of the current WZ
frame, the SI provides the important inter-frame correlation for the correlation noise statistics. In this paper, a novel algorithm is proposed for the SI refinement first. The proposed refinement algorithm iteratively
learns the difference between the already decoded information of the current WZ frame and the SI, and
makes a targeted refinement for the SI quality. Subsequently, a progressively refined correlation noise model
is proposed based on the novel SI refinement algorithm. The progressively refined WZ video coding is thus
achieved. The performance evaluations show that the proposed technique advances over the existing DVC
systems. The proposed technique provides an efficient way to improve the video compression performance
for the low-power wearable cameras in human-centered telehealth.

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More Information

Depositing User: Yonghong Peng

Identifiers

Item ID: 10171
Identification Number: https://doi.org/10.1109/ACCESS.2018.2850052
ISSN: 2169-3536
URI: http://sure.sunderland.ac.uk/id/eprint/10171
Official URL: https://doi.org/10.1109/ACCESS.2018.2850052

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Catalogue record

Date Deposited: 20 Nov 2018 10:25
Last Modified: 18 Dec 2019 16:07

Contributors

Author: Jia Yang
Author: Xiaohai He
Author: Linbo Qing
Author: Shuhua Xiong
Author: Yonghong Peng

University Divisions

Faculty of Technology
Faculty of Technology > FOT Executive

Subjects

Computing > Data Science
Computing > Artificial Intelligence

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