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Visual Attention in Objective Image Quality Assessment: Based on Eye-Tracking Data

Liu, Hantao; Heynderickx, Ingrid


Hantao Liu

Ingrid Heynderickx


Since the human visual system (HVS) is the ultimate assessor of image quality, current research on the design of objective image quality metrics tends to include an important feature of the HVS, namely, visual attention. Different metrics for image quality prediction have been extended with a computational model of visual attention, but the resulting gain in reliability of the metrics so far was variable. To better understand the basic added value of including visual attention in the design of objective metrics, we used measured data of visual attention. To this end, we performed two eye-tracking experiments: one with a free-looking task and one with a quality assessment task. In the first experiment, 20 observers looked freely to 29 unimpaired original images, yielding us so-called natural scene saliency (NSS). In the second experiment, 20 different observers assessed the quality of distorted versions of the original images. The resulting saliency maps showed some differences with the NSS, and therefore, we applied both types of saliency to four different objective metrics predicting the quality of JPEG compressed images. For both types of saliency the performance gain of the metrics improved, but to a larger extent when adding the NSS. As a consequence, we further integrated NSS in several state-of-the-art quality metrics, including three full-reference metrics and two no-reference metrics, and evaluated their prediction performance for a larger set of distortions. By doing so, we evaluated whether and to what extent the addition of NSS is beneficial to objective quality prediction in general terms. In addition, we address some practical issues in the design of an attention-based metric. The eye-tracking data are made available to the research community. © 2011 IEEE.


Liu, H., & Heynderickx, I. (2011). Visual Attention in Objective Image Quality Assessment: Based on Eye-Tracking Data. IEEE Transactions on Circuits and Systems for Video Technology, 21(7), 971-982.

Journal Article Type Article
Online Publication Date Mar 28, 2011
Publication Date 2011-07
Deposit Date Nov 13, 2014
Journal Ieee Transactions On Circuits And Systems For Video Technology
Print ISSN 1051-8215
Electronic ISSN 1558-2205
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 21
Issue 7
Pages 971-982
Keywords Eye tracking, image quality assessment, objective metric, saliency map, visual attention, REF 2014 submission**
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