Evaluating the Factors Affecting QoE of 360-Degree Videos and Cybersickness Levels Predictions in Virtual Reality

Electronics

Sadique Ahmad¹  Asad Ullah²  MuhammadShahid Anwar³  Wahab Khan³  Zesong Fei³  Jing Wang³ 

[1] Department of Computer Science, Bahria University Karachi Campus, Karachi 75260, Pakistan;Faculty of Computing, Riphah International University, Faisalabad 38000, Pakistan;School of Information and Electronics, Beijing Institute of Technology, Beijing 100080, China;

关键词: quality of experience;    360-degree videos;    virtual reality;    cybersickness;    ANN;   

DOI  :  10.3390/electronics9091530

来源: DOAJ

【 摘 要 】

360-degree Virtual Reality (VR) videos have already taken up viewers’ attention by storm. Despite the immense attractiveness and hype, VR conveys a loathsome side effect called “cybersickness” that often creates significant discomfort to the viewers. It is of great importance to evaluate the factors that induce cybersickness symptoms and its deterioration on the end user’s Quality-of-Experience (QoE) when visualizing 360-degree videos in VR. This manuscript’s intent is to subjectively investigate factors of high priority that affect a user’s QoE in terms of perceptual quality, presence, and cybersickness. The content type (fast, medium, and slow), the effect of camera motion (fixed, horizontal, and vertical), and the number of moving targets (none, single, and multiple) in a video can be the factors that may affect the QoE. The significant effect of such factors on end-user QoE under various stalling events (none, single, and multiple) is evaluated in a subjective experiment. The results from subjective experiments show a notable impact of these factors on end-user QoE. Finally, to label the viewing safety concern in VR, we propose a neural network-based QoE prediction method that can predict the degree of cybersickness influenced by 360-degree videos under various stalling events in VR. The performance accuracy of the proposed method is then compared against well-known Machine Learning (ML) algorithms and existing QoE prediction models. The proposed method achieved a 90% prediction accuracy rate and performed well against existing models and other ML methods.

【 授权许可】

Unknown