The Impact of Black Edge Artifact on QoE of the FOV-Based Cloud VR Services

被引:10
|
作者
Song, Jiarun [1 ]
Mao, Xionghui [1 ]
Yang, Fuzheng [2 ]
机构
[1] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China
[2] Xidian Univ, State Key Lab ISN, Xian 710071, Peoples R China
基金
中国国家自然科学基金;
关键词
Cloud computing; Games; Quality of experience; Rendering (computer graphics); Servers; Delays; Streaming media; Black edge; cloud VR; latency; user experience; virtual reality; VIDEO GAMES; QUALITY;
D O I
10.1109/TMM.2022.3232229
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cloud virtual reality (Cloud VR) services usually introduce high latency in rendering and streaming, resulting in a mismatch between the visual and vestibular systems, causing user sickness and dizziness during the service. To solve this problem, asynchronous rendering technology is usually used to provide smooth viewing. The asynchronous solution, on the other hand, will introduce another "black edge" (BE) artifact in the service, which frequently appears at the viewport's boundary with a black area when users turn their heads. This unwanted BE artifact also has an impact on the user's quality of experience (QoE). In this paper, we investigated the impact of the BE artifact on the user's QoE of the field of view (FOV) Cloud VR gaming services. The appearance of the BE artifact during the playing period was regarded as a series of BE events and the impact of BE artifact on the users' QoE was evaluated by accumulating the influence of the BE events during the whole playing period. More specifically, the user's QoE affected by a single BE event was first evaluated by combining the area ratio and duration of the BE artifact. Then, the QoE affected by multiple BE events was analyzed, where the cumulative influence of the previous BE events on the user's current QoE was evaluated. Finally, a unified event-based evaluation model was proposed to predict the user's time-varying QoE at any point in time. Experimental results showed that the proposed model performed exceptionally well in predicting the impact of BE artifact on the user's QoE.
引用
收藏
页码:8020 / 8035
页数:16
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