3D objective quality assessment of light field video frames

Tamboli, Roopak R., Kara, Peter A., Cserkaszky, Aron, Barsi, Attila, Martini, Maria G., Appina, Balasubramanyam, Channappayya, Sumohana S. and Jana, Soumya (2018) 3D objective quality assessment of light field video frames. In: 2018 - 3DTV-Conference : The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON); 03 - 05 Jun 2018, Stockholm, Sweden.

Full text available as:
[img]
Preview
Text
Kara-P-A-44440-AAM.pdf - Accepted Version

Download (681kB) | Preview

Abstract

With the rapid advances in light field displays and cameras, research in light field content creation, visualization, coding and quality assessment is now beyond a state of emergence; it has already emerged and started attracting a significant part of the scientific community. The capability of light field displays to offer glasses-free 3D experience simultaneously for multiple users has opened new avenues in subjective and objective quality assessment of light field image content, and video is also becoming research target of such quality evaluation methods. Yet it needs to be stated that while static light field contents have evidently received relatively more attention, the research on light field video content still remains largely unexplored. In this paper, we present results of the objective quality assessment of key frames extracted from light field video content. To this end, we use our own full-reference 3D objective quality metric.

Item Type: Conference or Workshop Item (Paper)
Event Title: 2018 - 3DTV-Conference : The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON)
Organising Body: Institute of Electrical and Electronics Engineers
Additional Information: Published in: 2018 - 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON) (IEEE Catalog Number: CFP1855B-ART) ISSN 2161-203X ISBN 9781538661253. The work in this paper was funded from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreements No 643072, Network QoE-Net and No 676401, European Training Network on Full Parallax Imaging.
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing
Faculty of Science, Engineering and Computing > School of Computer Science and Mathematics
Depositing User: Philip Keates
Date Deposited: 20 Nov 2019 08:29
Last Modified: 28 Jan 2020 10:06
DOI: https://doi.org/10.1109/3DTV.2018.8478557
URI: http://eprints.kingston.ac.uk/id/eprint/44440

Actions (Repository Editors)

Item Control Page Item Control Page