Study of 3D Virtual Reality Picture Quality

TL;DR

This paper presents a comprehensive study on 3D VR picture quality, including a new subjective database with eye tracking data, and evaluates existing quality assessment models to advance VR content quality prediction.

Contribution

It introduces a large VR quality database with subjective ratings and eye tracking data, and assesses the performance of existing IQA models on this new dataset.

Findings

Eye tracking data reveals gaze patterns related to perceived quality.

Existing IQA models show varying performance on VR content.

The database enables benchmarking and development of better VR quality models.

Abstract

Virtual Reality (VR) and its applications have attracted significant and increasing attention. However, the requirements of much larger file sizes, different storage formats, and immersive viewing conditions pose significant challenges to the goals of acquiring, transmitting, compressing and displaying high quality VR content. Towards meeting these challenges, it is important to be able to understand the distortions that arise and that can affect the perceived quality of displayed VR content. It is also important to develop ways to automatically predict VR picture quality. Meeting these challenges requires basic tools in the form of large, representative subjective VR quality databases on which VR quality models can be developed and which can be used to benchmark VR quality prediction algorithms. Towards making progress in this direction, here we present the results of an immersive 3D subjective image quality assessment study. In the study, 450 distorted images obtained from 15 pristine 3D VR images modified by 6 types of distortion of varying severities were evaluated by 42 subjects in a controlled VR setting. Both the subject ratings as well as eye tracking data were recorded and made available as part of the new database, in hopes that the relationships between gaze direction and perceived quality might be better understood. We also evaluated several publicly available IQA models on the new database, and also report a statistical evaluation of the performances of the compared IQA models.