VR Video Quality in the Wild

Investigating how people perceive virtual reality videos in the wild (i.e., those captured by everyday users) is a crucial and challenging task in VR-related applications due to complex authentic distortions localized in space and time. Existing panoramic video databases only consider synthetic distortions, assume fixed viewing conditions, and are limited in size. To overcome these shortcomings, we construct the VR Video Quality in the Wild (VRVQW) database, which is one of the first of its kind, and contains 502 user-generated videos with diverse content and distortion characteristics. Based on VRVQW, we conduct a formal psychophysical experiment to record the scanpaths and perceived quality scores from 139 participants under two different viewing conditions. We provide a thorough sta- tistical analysis of the recorded data, observing significant impact of viewing conditions on both human scanpaths and perceived quality. Moreover, we develop an objective quality assessment model for VR videos based on pseudocylindrical representation and convolution. Results on the proposed VRVQW show that our method is superior to existing video quality assessment models, only underperforming viewport-based models that otherwise rely on human scanpaths for projection. We have made the database and code available at https://github.com/limuhit/VR-Video-Quality-in-the-Wild.

探讨人们在现实场景中感知虚拟现实视频（即由普通用户所拍摄的视频）的方法，对于虚拟现实相关应用而言，是一项至关重要且充满挑战的任务。这是因为虚拟现实视频在空间和时间维度上存在着复杂的真实扭曲。现有的全景视频数据库仅考虑合成扭曲，假设固定的观看条件，且规模有限。为了克服这些不足，我们构建了野外观测下的虚拟现实视频质量数据库（VRVQW），该数据库是首屈一指的，其中包含502个具有多样内容和扭曲特征的由用户生成视频。基于VRVQW，我们进行了一项正式的心理物理实验，记录了139名参与者在两种不同观看条件下的扫描路径和感知质量评分。我们对所记录的数据进行了详尽统计分析，观察到观看条件对人类的扫描路径和感知质量具有显著影响。此外，我们基于伪圆柱表示和卷积开发了针对虚拟现实视频的客观质量评估模型。在所提出的VRVQW上，我们的方法在基于视窗的模型之外，在依赖人类扫描路径进行投影的情况下表现略逊于现有的视频质量评估模型。我们将数据库和代码发布在https://github.com/limuhit/VR-Video-Quality-in-the-Wild。