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.