Computing contrast ratio in images using local content information

It is well know that a measure of contrast in images is not yet fully defined. The conventional measures of contrast consist of global computations and therefore they have a poor performance. At the same time image quality assessment is often based on quantifying the visibility between a structure of interest or foreground and its surrounding background, i.e., the contrast ratio. Then, a high quality image is the one in which structures of interest are well distinguishable from the background. Therefore, the computation of contrast ratio is important in automatic image quality assessment and it should be computed locally taking into account the local distribution of pixel values. We estimate the contrast ratio by using Weber contrast in local image patches. The main contribution of this work lies in the characterization of local distribution of pixel values which is used for computing the contrast ratio. Here, local image patches are characterized by bimodal histograms representing a set of pixels which are likely to be inside the foreground and another set likely to be in the background. The local contrast ratio is estimated using the ratio between mean intensity values of each mode of the histogram. Our experimental results over two public image databases show that the proposed method is able to accurately predict changes of quality due to contrast decrements (Pearson correlations higher than 90%).