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Quality assessment for virtual reality technology based on real scene
journal contribution
posted on 2017-03-23, 16:35 authored by Bin Jiang, Jiachen Yang, Na Jiang, Zhihan Lv, Qinggang MengQinggang MengVirtual reality technology is a new display technology, which provides users with real viewing experience. As known, most of the virtual reality display through stereoscopic images. However, image quality will be influenced by the collection, storage and transmission process. If the stereoscopic image quality in the virtual reality technology is seriously damaged, the user will feel uncomfortable, and this can even cause healthy problems. In this paper, we establish a set of accurate and effective evaluations for the virtual reality. In the preprocessing, we segment the original reference and distorted image into binocular regions and monocular regions. Then, the Information-weighted SSIM (IW-SSIM) or Information-weighted PSNR (IW-PSNR) values over the monocular regions are applied to obtain the IW-score. At the same time, the Stereo-weighted-SSIM (SW-SSIM) or Stereo-weighted-PSNR (SW-PSNR) can be used to calculate the SW-score. Finally, we pool the stereoscopic images score by combing the IW-score and SW-score. Experiments show that our method is very consistent with human subjective judgment standard in the evaluation of virtual reality technology.
Funding
This research is partially supported by National Natural Science Foundation of China (Nos. 61471260 and 61271324) and Natural Science Foundation of Tianjin (No. 16JCYBJC16000).
History
School
- Science
Department
- Computer Science
Published in
Neural Computing and ApplicationsPages
1 - 10Citation
JIANG, B. ... et al, 2018. Quality assessment for virtual reality technology based on real scene. Neural Computing and Applications, 29(5), pp. 1199-1208.Publisher
© The Natural Computing Applications Forum. Published by SpringerVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2016-12-19Publication date
2018Notes
This is a post-peer-review, pre-copyedit version of an article published in Neural Computing and Applications. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00521-016-2828-0ISSN
0941-0643eISSN
1433-3058Publisher version
Language
- en