Perceptual Quality Assessment of Omnidirectional Images as Moving Camera Videos

TL;DR

This paper investigates how VR viewing conditions affect perceived quality of 360-degree images and proposes a new computational framework that incorporates user behavior and viewing conditions for objective quality assessment.

Contribution

It introduces a psychophysical experiment analyzing VR viewing behaviors and develops a novel quality assessment framework that models these behaviors for better accuracy.

Findings

Viewing conditions significantly influence perceived quality.

The proposed framework outperforms existing models on VR quality databases.

User exploration time and starting point are key factors in quality perception.

Abstract

Omnidirectional images (also referred to as static 360{\deg} panoramas) impose viewing conditions much different from those of regular 2D images. How do humans perceive image distortions in immersive virtual reality (VR) environments is an important problem which receives less attention. We argue that, apart from the distorted panorama itself, two types of VR viewing conditions are crucial in determining the viewing behaviors of users and the perceived quality of the panorama: the starting point and the exploration time. We first carry out a psychophysical experiment to investigate the interplay among the VR viewing conditions, the user viewing behaviors, and the perceived quality of 360{\deg} images. Then, we provide a thorough analysis of the collected human data, leading to several interesting findings. Moreover, we propose a computational framework for objective quality assessment of 360{\deg} images, embodying viewing conditions and behaviors in a delightful way. Specifically, we first transform an omnidirectional image to several video representations using different user viewing behaviors under different viewing conditions. We then leverage advanced 2D full-reference video quality models to compute the perceived quality. We construct a set of specific quality measures within the proposed framework, and demonstrate their promises on three VR quality databases.