论文标题
野外虚拟现实视频的感知质量评估
Perceptual Quality Assessment of Virtual Reality Videos in the Wild
论文作者
论文摘要
研究人们如何在野外感知虚拟现实（即每天用户捕获的视频）如何在与VR相关的应用中的一项至关重要且挑战性的任务，这是由于在时空和时间上定位的复杂真实的扭曲。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，并且大小有限。为了克服这些缺点，我们在野外（VRVQW）数据库中构建了VR视频质量，其中包含$ 502 $用户生成的视频，具有多种内容和失真特征。基于VRVQW，我们进行了正式的心理物理实验，以记录在两个不同观看条件下的$ 139 $参与者的质量分数。我们对记录的数据进行了详尽的统计分析，观察到观看条件对人类扫描和感知质量的重大影响。此外，我们基于伪内表示和卷积为VR视频开发了客观质量评估模型。拟议的VRVQW的结果表明，我们的方法优于现有的视频质量评估模型。我们已经在https://github.com/limuhit/vr-video-quality-in-the-wild上提供了数据库和代码。
Investigating how people perceive virtual reality (VR) 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, containing $502$ 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 statistical 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. We have made the database and code available at https://github.com/limuhit/VR-Video-Quality-in-the-Wild.
