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

Mikko Nuutinen; Toni Virtanen; Pirkko Oittinen

doi:10.1117/1.JEI.23.6.061111

15 September 2014 Image feature subsets for predicting the quality of consumer camera images and identifying quality dimensions

Mikko Nuutinen, Toni Virtanen, Pirkko Oittinen

Author Affiliations +

Journal of Electronic Imaging, Vol. 23, Issue 6, 061111 (September 2014). https://doi.org/10.1117/1.JEI.23.6.061111

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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Mikko Nuutinen, Toni Virtanen, and Pirkko Oittinen "Image feature subsets for predicting the quality of consumer camera images and identifying quality dimensions," Journal of Electronic Imaging 23(6), 061111 (15 September 2014). https://doi.org/10.1117/1.JEI.23.6.061111

Published: 15 September 2014

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