Perceptual Quality Assessment of Virtual Reality Videos in the Wild
This work addresses the challenge of evaluating VR video quality in real-world scenarios for VR application developers and researchers, though it is incremental as it builds on existing quality assessment frameworks.
The authors tackled the problem of assessing perceptual quality for user-generated virtual reality videos by constructing the VRVQW database with 502 videos and conducting a psychophysical experiment with 139 participants, resulting in an objective quality assessment model that outperforms existing methods on this dataset.
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.