NDLI: Improved foveation- and saliency-based visual attention prediction under a quality assessment task

Content Provider	IEEE Xplore Digital Library
Author	Gide, M.S. Karam, L.J.
Copyright Year	2012
Description	Author affiliation: School of Electrical, Computer and Energy Engineering, Arizona State University Tempe, 85287-5706, USA (Gide, M.S.; Karam, L.J.)
Abstract	Image quality assessment is one application out of many that can be aided by the use of computational saliency models. Existing visual saliency models have not been extensively tested under a quality assessment context. Also, these models are typically geared towards predicting saliency in non-distorted images. Recent work has also focussed on mimicking the human visual system in order to predict fixation points from saliency maps. One such technique (GAFFE) that uses foveation has been found to perform well for non-distorted images. This work extends the foveation framework by integrating it with saliency maps from well known saliency models. The performance of the foveated saliency models is evaluated based on a comparison with human ground-truth eye-tracking data. For comparison, the performance of the original non-foveated saliency predictions is also presented. It is shown that the integration of saliency models with a foveation based fixation finding framework significantly improves the prediction performance of existing saliency models over different distortion types. It is also found that the information maximization based saliency maps perform the best consistently over different distortion types and levels under this foveation based framework.
Starting Page	200
Ending Page	205
File Size	1299310
Page Count	6
File Format	PDF
ISBN	9781467307246
e-ISBN	9781467307260
e-ISBN	9781467307253
DOI	10.1109/QoMEX.2012.6263871
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-05
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Foveation Gaze Noise Abstracts Educational institutions High temperature superconductors Quality Assessment Visual Attention Sun Information Maximization
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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