Perceptual Quality Assessment of Virtual Reality Videos in the Wild

TL;DR

This paper introduces a new VR video quality assessment database captured in real-world conditions, analyzes human perception and scanpaths, and develops a model that outperforms existing methods.

Contribution

The creation of the VRVQW database with diverse user-generated VR videos and the development of a superior quality assessment model based on pseudocylindrical representation.

Findings

Viewing conditions significantly affect perceived quality and scanpaths.

The proposed model outperforms existing VR video quality assessment methods.

The database and code are publicly available for further research.

Abstract

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.