Perceptual Quality Assessment of Omnidirectional Images

TL;DR

This paper develops a new database and evaluates perceptual quality assessment methods for omnidirectional images in VR, considering human visual attention and movement data to improve quality evaluation accuracy.

Contribution

It introduces the first comprehensive omnidirectional IQA database with subjective ratings and eye/head movement data, and analyzes the performance of existing IQA metrics in VR environments.

Findings

State-of-the-art IQA metrics show limitations on omnidirectional images.

Visual attention significantly impacts perceived image quality.

New observations differ from traditional IQA assessments.

Abstract

Omnidirectional images and videos can provide immersive experience of real-world scenes in Virtual Reality (VR) environment. We present a perceptual omnidirectional image quality assessment (IQA) study in this paper since it is extremely important to provide a good quality of experience under the VR environment. We first establish an omnidirectional IQA (OIQA) database, which includes 16 source images and 320 distorted images degraded by 4 commonly encountered distortion types, namely JPEG compression, JPEG2000 compression, Gaussian blur and Gaussian noise. Then a subjective quality evaluation study is conducted on the OIQA database in the VR environment. Considering that humans can only see a part of the scene at one movement in the VR environment, visual attention becomes extremely important. Thus we also track head and eye movement data during the quality rating experiments. The original and distorted omnidirectional images, subjective quality ratings, and the head and eye movement data together constitute the OIQA database. State-of-the-art full-reference (FR) IQA measures are tested on the OIQA database, and some new observations different from traditional IQA are made.