Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues

Author:

Coskun Sahin1ORCID,Nur Yilmaz Gokce2,Battisti Federica3,Alhussein Musaed4,Islam Saiful2ORCID

Affiliation:

1. Department of Electrical-Electronics Engineering, Graduate School of Natural and Applied Sciences, Gazi University, Ankara 06560, Turkey

2. Department of Computer Engineering, TED University, Ankara 06420, Turkey

3. Department of Information Engineering, University of Padova, 35131 Padova, Italy

4. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia

Abstract

A three-dimensional (3D) video is a special video representation with an artificial stereoscopic vision effect that increases the depth perception of the viewers. The quality of a 3D video is generally measured based on the similarity to stereoscopic vision obtained with the human vision system (HVS). The reason for the usage of these high-cost and time-consuming subjective tests is due to the lack of an objective video Quality of Experience (QoE) evaluation method that models the HVS. In this paper, we propose a hybrid 3D-video QoE evaluation method based on spatial resolution associated with depth cues (i.e., motion information, blurriness, retinal-image size, and convergence). The proposed method successfully models the HVS by considering the 3D video parameters that directly affect depth perception, which is the most important element of stereoscopic vision. Experimental results show that the measurement of the 3D-video QoE by the proposed hybrid method outperforms the widely used existing methods. It is also found that the proposed method has a high correlation with the HVS. Consequently, the results suggest that the proposed hybrid method can be conveniently utilized for the 3D-video QoE evaluation, especially in real-time applications.

Funder

King Saud University

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging

Reference78 articles.

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4. Su, Z., Li, D., Ren, H., and Chen, L. (2017, January 29–31). Evaluation of depth perception based on binocular stereo vision. Proceedings of the 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Guilin, China.

5. Blind Stereoscopic Video Quality Assessment: From Depth Perception to Overall Experience;Chen;IEEE Trans. Image Process.,2018

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