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Distinguishing artwork from digital photographs is a simple task for a human observer. However, imparting this ability to a machine requires quantification of the quality of naturalness. In particular, the quality of ‘glossiness’ of a scene in a digital photograph as opposed to that in artwork or a painting of the same scene, is a challenging parameter to quantify. In this paper, a classification-based approach is proposed to quantify this glossiness of an image. The authors propose and validate the hypothesis that features extracted from the curvelet transform contain information regarding glossiness of an image. By training a support vector machine (SVM) classifier using curvelet coefficient features of images of varying naturalness, a framework to predict no-reference glossiness index for any general query image is proposed. The reliability of the proposed metric was then gauged by obtaining its correlation with subjective scores on naturalness provided by human observers. Results exhibit noteworthy performance of this metric in representing image glossiness, and could be further improved by using a larger training database and advanced classifiers.