Evaluating the Factors Affecting QoE of 360-Degree Videos and Cybersickness Levels Predictions in Virtual Reality
Evaluating the Factors Affecting QoE of 360-Degree Videos and Cybersickness Levels Predictions in Virtual Reality
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DOI：
10.3390/electronics9091530
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发表时间：
2020-09-01
期刊：
影响因子：
2.9
通讯作者：
Fei, Zesong
中科院分区：
文献类型：
--
作者：
Anwar, Muhammad Shahid;Wang, Jing;Fei, Zesong
360 度虚拟现实 (VR) 视频已经迅速吸引了观众的注意力。尽管虚拟现实具有巨大的吸引力和炒作，但它也带来了一种令人厌恶的副作用，称为“网络病”，常常给观众带来极大的不适。在 VR 中可视化 360 度视频时，评估引发晕眩症状的因素及其对最终用户体验质量 (QoE) 的恶化具有重要意义。本手稿的目的是主观地调查影响用户 QoE 的感知质量、存在感和晕机症等高优先级因素。视频中的内容类型（快、中、慢）、摄像机运动的效果（固定、水平和垂直）以及视频中移动目标的数量（无、单个和多个）都可能是影响 QoE 的因素。在主观实验中评估了这些因素在各种停顿事件（无停顿、单次停顿和多次停顿）下对最终用户 QoE 的显着影响。主观实验的结果表明这些因素对最终用户 QoE 有显着影响。最后，为了标记 VR 中的观看安全问题，我们提出了一种基于神经网络的 QoE 预测方法，该方法可以预测 VR 中各种停顿事件下 360 度视频影响的晕动程度。然后将所提出方法的性能准确性与众所周知的机器学习 (ML) 算法和现有 QoE 预测模型进行比较。所提出的方法实现了 90% 的预测准确率，并且与现有模型和其他 ML 方法相比表现良好。
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.