Subjective Quality Assessment of User-Generated $360^{\circ}$ Videos

Abstract

In this poster, we establish one of the largest virtual reality (VR) video database, containing 502 user-generated videos with rich content and commingled authentic distortions (often localized in space and time). We capture viewing behaviors (i.e., scanpaths) of 139 users, and collect their opinion scores of perceived qual-ity under four different viewing conditions (two starting points $\times$ two exploration times). We provide a thorough statistical analysis of recorded data, resulting in several interesting observations, which reveal how viewing conditions affect human behav-iors and perceived quality. The database is available at https://github.com/Yao-Yiru/VR-Video-Database.