No-reference quality assessment of enhanced images

Image enhancement is a popular technique, which is widely used to improve the visual quality of images. While image enhancement has been extensively investigated, the relevant quality assessment of enhanced images remains an open problem, which may hinder further development of enhancement techniques. In this paper, a no-reference quality metric for digitally enhanced images is proposed. Three kinds of features are extracted for characterizing the quality of enhanced images, including non-structural information, sharpness and naturalness. Specifically, a total of 42 perceptual features are extracted and used to train a support vector regression (SVR) model. Finally, the trained SVR model is used for predicting the quality of enhanced images. The performance of the proposed method is evaluated on several enhancement-related databases, including a new enhanced image database built by the authors. The experimental results demonstrate the efficiency and advantage of the proposed metric.