Big video data for light-field-based 3D telemedicine

Xiang, Wei and Wang, Gengkun and Pickering, Mark and Zhang, Yongbing (2016) Big video data for light-field-based 3D telemedicine. IEEE Network, 30 (3). pp. 30-38. ISSN 0890-8044

Abstract

Big data and 3D technologies have been successfully leveraged in a variety of industries to improve their efficiency and quality. The healthcare sector has lagged in the uptake of these new technologies. In this article, we propose a novel light field (LF)-based 3D telemedicine system. The proposed system is able to provide a life-like tele-consultation experience that provides a quality of experience far beyond conventional 2D telemedicine systems. In addition, its embedded 3D data in light field video (LFV) format can also facilitate a higher level of big data analysis, so-called big LFV data analysis. To solve the challenges in storage and analysis of LFV, we extend the standard multi-view video coding (MVC) approach to LF-MVC, which is able to achieve up to a 23 percent higher compression rate when compared to standard MVC. Furthermore, a big data analysis framework is proposed to integrate LFV into conventional telemedicine analysis, which can achieve improved classification, statistics gathering, prediction, and cognitive analysis for healthcare applications.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering
Date Deposited: 06 Jul 2016 04:45
Last Modified: 06 Mar 2018 06:35
Uncontrolled Keywords: big data; data analysis; health care; telemedicine; light filed
Fields of Research : 10 Technology > 1005 Communications Technologies > 100509 Video Communications
Socio-Economic Objective: C Society > 92 Health > 9299 Other Health > 929999 Health not elsewhere classified
Identification Number or DOI: 10.1109/MNET.2016.7474341
URI: http://eprints.usq.edu.au/id/eprint/29354

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