Histogram modification based enhancement along with contrast-changed image quality assessment

Contrast is the difference in visual characteristics which make an object more recognizable. Despite the significance of contrast enhancement (CE) in image processing applications, few attempts have been made on assessment of the contrast change. In this paper, a visual information fidelity-based contrast change metric (VIF-CCM) is presented which includes visual information fidelity (VIF), local entropy, correlation coefficient, and mean intensity measures. The validation results of the presented VIF-CCM show its efficiency and superiority over the state-of–the-arts image quality assessment metrics. A histogram modification based contrast enhancement (HMCE) method is also proposed in this paper. The proposed HMCE comprises of four steps: segmentation of the input image, employing a set of weighting constraints, applying the combination of adaptive gamma correction and equalization on modified histogram, and optimization the value of the constraint weights by PSO algorithm. Experimental results demonstrate that the proposed HMCE outperforms other existing CE methods subjectively and objectively.