Image Quality Assessment for Color Correction Based on Color Contrast Similarity and Color Value Difference

Abstract

Color correction plays an important role in the image processing field. But substantial research on the assessment of color correction is still insufficient. In this paper, we present an image quality assessment metric for color correction. It assesses the color consistency between the reference and target/result images of color correction according to their color contrast similarity and color value difference. Both the average difference and difference span are considered during the assessment. To compensate for the scene difference between the reference and target/result images of color correction, we propose to use an image registration algorithm to build their matching relationship, upon which a matching image is built. The matching image has the same scene as the target image and the same color feature as the reference image, and thus the matching image is regarded as the real reference image of our color correction assessment. Furthermore, we combine a confidence map of the matching image and a saliency map of the target/result image as a weighting map for assessment, which helps to improve the consistency between the objective and subjective assessment results. The experimental results show that our color correction assessment metric has better correlation, accuracy, and monotonicity with users’ subjective scores than 19 state-of-the-art metrics.