Special Section on Image/Video Quality and System Performance

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

[+] Author Affiliations
Mikko Nuutinen

Aalto University, Department of Media Technology, P.O. Box 15500, Espoo FI-00076, Finland

University of Helsinki, Institute of Behavioural Sciences, P.O. Box 9, Helsinki FI-00014, Finland

Toni Virtanen

University of Helsinki, Institute of Behavioural Sciences, P.O. Box 9, Helsinki FI-00014, Finland

Pirkko Oittinen

Aalto University, Department of Media Technology, P.O. Box 15500, Espoo FI-00076, Finland

J. Electron. Imaging. 23(6), 061111 (Sep 15, 2014). doi:10.1117/1.JEI.23.6.061111
History: Received March 28, 2014; Revised July 12, 2014; Accepted August 26, 2014
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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.

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Citation

Mikko Nuutinen ; Toni Virtanen and Pirkko Oittinen
"Image feature subsets for predicting the quality of consumer camera images and identifying quality dimensions", J. Electron. Imaging. 23(6), 061111 (Sep 15, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.6.061111


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