Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Towards a reliable collection of eye-tracking data for image quality research: challenges, solutions and applications

Zhang, Wei and Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 2017. Towards a reliable collection of eye-tracking data for image quality research: challenges, solutions and applications. IEEE Transactions on Image Processing 26 (5) , pp. 2424-2437. 10.1109/TIP.2017.2681424

[thumbnail of 07875488.pdf]
Preview
PDF - Published Version
Available under License Creative Commons Attribution.

Download (7MB) | Preview

Abstract

Image quality assessment potentially benefits from the addition of visual attention. However, incorporating aspects of visual attention in image quality models by means of a perceptually optimized strategy is largely unexplored. Fundamental challenges, such as how visual attention is affected by the concurrence of visual signals and their distortions; whether visual attention affected by distortion or that driven by the original scene only should be included in an image quality model; and how to select visual attention models for the image quality application context, remain. To shed light on the above unsolved issues, designing and performing eye-tracking experiments are essential. Collecting eye-tracking data for the purpose of image quality study is so far confronted with a bias due to the involvement of stimulus repetition. In this paper, we propose a new experimental methodology to eliminate such inherent bias. This allows obtaining reliable eye-tracking data with a large degree of stimulus variability. In fact, we first conducted 5760 eye movement trials that included 160 human observers freely viewing 288 images of varying quality. We then made use of the resulting eye-tracking data to provide insights into the optimal use of visual attention in image quality research. The new eye-tracking data are made publicly available to the research community.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical & Electronic Engineers
ISSN: 1057-7149
Date of First Compliant Deposit: 3 May 2017
Date of Acceptance: 22 February 2017
Last Modified: 04 May 2023 16:27
URI: https://orca.cardiff.ac.uk/id/eprint/99163

Citation Data

Cited 33 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics