In this paper, we propose a new enhancement method for color images. In color image processing, hue preserving is required. The proposed method is performed into HSI color space whose gamut is same as RGB color space. The differential gray-level histogram equalization (DHE) is effective for gray scale images. The proposed method is an extension version of the DHE for color images, and furthermore, the enhancement degree is variable by introducing two parameters. Since our processing method is applied to not only intensity but also saturation, the contrast and the colorfulness of the output image can be varied. It is an important issue how to determine the two parameters. Thus, we give the guideline for how to decide the two parameters. By using the guideline, users can easily obtain their own enhancement images.

Image retouching is fundamental in photography, which is widely used to improve the perceptual quality of a low-quality image. Traditional image quality metrics are designed for degraded images, so they are limited in evaluating the quality of retouched images. This letter presents a RETouched Image QUality Evaluation (RETIQUE) algorithm by measuring structure and color changes between the original and retouched images. Structure changes are measured by gradient similarity. Color colorfulness and saturation are utilized to measure color changes. The overall quality score of a retouched image is computed as the linear combination of gradient similarity and color similarity. The performance of RETIQUE is evaluated on a public Digitally Retouched Image Quality (DRIQ) database. Experimental results demonstrate that the proposed metric outperforms the state-of-the-arts.

The visual attributes, brightness, colorfulness, and hue, are affected by chromaticity coordinates, excitation purity, and luminance of background, and these attributes are changed according as intensity of illumination changes. Therefore there is a need to correct display's conditions by controlling image factors such as luminance offset, contrast, color gain, and gamma to maintain the perceived display quality. The focus of our study is on the formulation of a simpler and more effective model for color and tone reproduction for digital imaging systems to utilize color appearance models. It can be applicable to optimum color display conditions to give comfort and consistency visually in various ambient conditions.

We investigated the effect of viewing conditions on human visual perception of colorfulness and constructed a mathematical model for colorfulness by fitting regression curves to LUTCHI color appearance data. This model enables the simple and effective prediction of the relative appearance attribute of colorfulness as a function of brightness and saturation, taking into account a range of viewing conditions, and hence provides a method of image reproduction for home display systems that can be easily adapted according to indoor viewing conditions.