Learning Based Contrast Enhancement Evaluation Using Cartoon Texture Decomposition

Contrast enhancement is an important processing task needed to improve the overall perceptual quality of an image. Ideally, a contrast enhancement method should neither affect the naturalness of images nor introduce other unwanted artifacts when improving perceptual quality. While humans are able to detect any improvement or such unwanted side-effects relatively easily, designing an objective metric that does the same is still a challenge. Most existing contrast enhancement evaluation (CEE) metrics do not take into consideration all these important criteria when evaluating quality. In this work, we propose a new measure for CEE that combines some of the carefully selected existing metrics effectively. Each of the selected metrics tackles only one of the important criteria of an effective contrast enhancement method such as structure preservation or contrast improvement. For this novel fusion-based metric, we have further exploited the full potential of the selected metrics by applying them on decomposed image using cartoon-texture decomposition. The results on two CEE databases show the effectiveness of our proposed metric.