Subjective and Viewport-based Objective Quality Assessment of 360-degree videos

Abstract

Visual distortions in processed 360-degree visual content and consumed through head-mounted displays (HMDs) are perceived very differently when compared to traditional 2D content. To better understand how compression-related artifacts affect the overall perceived quality of 360-degree videos, this paper presents a subjective quality assessment study and analyzes the performance of objective metrics to correlate with the gathered subjective scores. In contrast to previous related work, the proposed study focuses on the equiangular cubemap projection and includes specific visual distortions (blur, blockiness, H.264 compression, and cubemap seams) on both monoscopic and stereoscopic sequences. The objective metrics performance analysis is based on metrics computed in both the projection domain and the viewports, which is closer to what the user sees. The results show that overall objective metrics computed on viewports are more correlated with the subjective scores in our dataset than the same metrics computed in the projection domain. Moreover, the proposed dataset and objective metrics analysis serve as a benchmark for the development of new perception-optimized quality assessment algorithms for 360-degree videos, which is still a largely open research problem.