Skin-tone has been an active research subject in photographic colour reproduction. There is a consistent conclusion that preferred skin colours are different from actual skin colours. However, preferred skin colours found from different studies are somewhat different. To have a solid understanding of skin colour preference of digital photographic images, psychophysical experiments were conducted to determine a preferred skin colour region and to study interobserver variation and tolerance of preferred skin colours. In the first experiment, a preferred skin colour region is searched on the entire skin colour region. A set of nine predetermined colour centers uniformly sampled within the skin colour ellipse in CIELAB a*b* diagram is used to morph skin colours of test images. Preferred skin colour centers are found through the experiment. In a second experiment, a twice denser sampling of nine skin colour centers around the preferred skin colour center determined in the first experiment are generated to repeat the experiment using a different set of test images and judged by a different panel of observers. The results from both experiments are compared and final preferred skin colour centers are obtained. Variations and hue and chroma tolerances of the observer skin colour preference are also analysed.

Colour preference adjustment is an essential step for colour image enhancement and perceptual gamut mapping. In colour reproduction for pictorial images, properly shifting colours away from their colorimetric originals may produce more preferred colour reproduction result. Memory colours, as a portion of the colour regions for colour preference adjustment, are especially important for preference colour reproduction. Identifying memory colours or modelling the memory colour region is a basic step to study preferred memory colour enhancement. In this study, we first created gamut for each memory colour region represented as a convex hull, and then used the convex hull to guide mathematical modelling to formulate the colour region for colour enhancement.

Three elliptical skin color models are presented for skin color detection. The first one is to model the skin color cluster using a single ellipse ignoring the lightness dependency. It is simple and efficient, and the skin color detection accuracy may be adequate for many applications. In the second model, the skin color ellipse is adapted to different lightness levels to better fit the shape of the skin color cluster. The model is more complex to train, and the computation efficiency is lower, but the skin color detection accuracy is considerably higher. In the third method, an ellipsoid is trained to fit the skin color cluster. It is almost as simple to train as the first model, but the skin color detection accuracy is higher. Having skin color detection accuracy almost as high as the second model, this model is easier to train and may be more efficient in computation.

Skin tones are the most important colors among the memory color category. Reproducing skin colors pleasingly is an important factor in photographic color reproduction. Moving skin colors toward their preferred skin color center improves the color preference of skin color reproduction. Several methods to morph skin colors to a smaller preferred skin color region has been reported in the past. In this paper, a new approach is proposed to further improve the result of skin color enhancement. An ellipsoid skin color model is applied to compute skin color probabilities for skin color detection and to determine a weight for skin color adjustment. Preferred skin color centers determined through psychophysical experiments were applied for color adjustment. Preferred skin color centers for dark, medium, and light skin colors are applied to adjust skin colors differently. Skin colors are morphed toward their preferred color centers. A special processing is applied to avoid contrast loss in highlight. A 3-D interpolation method is applied to fix a potential contouring problem and to improve color processing efficiency. An psychophysical experiment validates that the method of preferred skin color enhancement effectively identifies skin colors, improves the skin color preference, and does not objectionably affect preferred skin colors in original images.

In the process of gamut mapping from monitor display into printer hardcopy in CIE L*a*b* color space, blue is tend to map to purple. This paper presents a new approach to solve the perceived blue hue shift problem. By this approach, the entire color gamut is divided into four regions: a non-blue region, a blue-region, and two in-between regions. The segmentation of the four regions is based on the hue angle in CIE L*a*b* color space. Different color spaces are applied to different regions for gamut mapping. In the non-blue region, CIE L*a*b* color space is applied for gamut mapping. In the blue region, CIE L*u*v* color space is applied to eliminate the perceived blue shift. In the two in-between regions, both color spaces are used for gamut mapping, and a weighting function is applied for smooth transaction.Three mapping techniques to test this mapping approach are: lightness rescaling followed by croma compression in constant lightness direction, lightness rescaling followed by croma compression along the straight line connected by the current color and the mid-point of the achromatic line, and lightness rescaling followed by minimum distance mapping. The experimental results show that the blue shift disappears in the blue region, and the color mapping is not changed in the non-blue region.This approach can be extended to applying more than two color spaces for gamut mapping. It is also suitable for optimal gamut mapping and preference gamut mapping.