Nowadays, the color-related aspect of image difference assessment has become an active area in the research of color science and imaging technology due to its wide range of applications such as color correction, color quantization, color image similarity and retrieval, image segmentation, gamut mapping, among others. For instance, 

• In multiview imaging, color correction is used to eliminate color inconsistencies between views. Then, the assessment of color corrected images can be used to find the color correction algorithm that produces the smallest difference in terms of color. 

• Color image similarity and retrieval is a process where all images with similar color composition to a query image are retrieved from a database. Thus, the assessment of CDs between images is very important to obtain those images with the minimum perceived CD with respect to the query image. 

• Gamut mapping and color quantization algorithms replace pixel colors following certain criteria to ensure a good correspondence in terms of color between an original image and its reproduction. That is, CD assessment can be used to find the quantization step size and/or the range of displayable colors to obtain the reproduction with the minimum perceived CD. 

• Color image segmentation divides images into regions displaying homogeneous colors. Hence, a CD measure can be used to find the regions with minimum perceived CD between pixels within the same region.

In search for an adequate solution of the problem of computing color differences in natural scene color images, we propose a measure based on the fact that humans assess the differences in image color by comparing small image patches of similar texture.

After dividing the image into a set of unique texture patches using the uLBP descriptors, we are ready to perform the color comparison independently in each homogeneous textured patch. In this case, we can use one of the image CD indices  available in the state of the art. Particularly, the statistics used in chroma spread and chroma extreme CD indices proposed by Pinson and Wolf have shown to be good measures of the change of spread in the color distribution and severe color differences, respectively. Accordingly, we propose to measure  the CDs in the resulting homogeneous textured patches using the linear combination of the chroma spread and chroma extreme indices because they capture color distribution parameters relevant to the humans. For computing the differences in the intensity channel, we use the well-known structural similarity index measure (SSIM).