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

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.

I. Introduction

Nowadays, many images of similar scenes are captured. These images differ in color or brightness. The differences are caused by various factors such as the time at which they are captured, light source position and intensity changes, and differences in aperture size and shooting angle. Color correction algorithms aim to correct color differences in two or more images/videos (as shown in Fig. 1). Color correction algorithms have been widely used and have three main categories of application: color transfer, image/video stitching, and stereoscopic 3D (S3D) image/video and multiview video (MMV) quality enhancement. Fig. 1.

Example of color correction. The reference image is used to correct the color of the target image and obtain the result image. (a) Reference image. (b) Target image. (c) Result image.