Novel color palettization scheme for preserving important colors

Abstract

Color palettization is the process that converts an input color image having a large set of possible colors to an output color image having a reduced set of palette colors. For example, a typical input 24-bit input image has possibly millions of colors, whereas a typical color palette has only 256 colors. It is desirable to determine the set of palette colors based on the distribution of colors in the input image. Furthermore, it is also desirable to preserve important colors such as human skin tones in the palettized image. We propose a novel scheme to accomplish these goals through supplementing the distribution of input colors by a distribution of selected important colors. In particular, skin color supplementation is achieved by appending to the input image skin tone patches generated from statistical sampling of the skin color probability density function. A major advantage of this scheme is that explicit skin detection, which can be error-prone and time consuming is avoided. In addition, this scheme can be used with any color palettization algorithms. Subjective evaluation has shown the efficacy of this scheme.