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Journal of Electronic Imaging

Image feature subsets for predicting the quality of consumer camera images and identifying quality dimensions
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Paper Abstract

Image-quality assessment measures are largely based on the assumption that an image is only distorted by one type of distortion at a time. These conventional measures perform poorly if an image includes more than one distortion. In consumer photography, captured images are subject to many sources of distortions and modifications. We searched for feature subsets that predict the quality of photographs captured by different consumer cameras. For this, we used the new CID2013 image database, which includes photographs captured by a large number of consumer cameras. Principal component analysis showed that the features classified consumer camera images in terms of sharpness and noise energy. The sharpness dimension included lightness, detail reproduction, and contrast. The support vector regression model with the found feature subset predicted human observations well compared to state-of-the-art measures.

Paper Details

Date Published: 15 September 2014
PDF: 17 pages
J. Electron. Imaging. 23(6) 061111 doi: 10.1117/1.JEI.23.6.061111
Published in: Journal of Electronic Imaging Volume 23, Issue 6
Show Author Affiliations
Mikko Nuutinen, Aalto Univ. (Finland)
Univ. of Helsinki (Finland)
Toni Virtanen, Univ. of Helsinki (Finland)
Pirkko Oittinen, Aalto Univ. School of Science and Technology (Finland)


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