In this letter, we propose a new color quantization method suppressing saturation decrease. In the proposed method, saturation-based weight and intensity-based weight are used so that vivid colors are selected as the representative colors preferentially. Experiments show that the proposed method tends to select vivid colors even if they occupy only a small area in the image.

In this letter, we propose a color quantization method based on saliency. In the proposed method, the salient colors are selected as representative colors preferentially by using saliency as weights. Through experiments, we verify the effectiveness of the proposed method.

A method obtaining a monochrome image which can rebuild colors is proposed. In this method, colors in an input image are quantized under a lightness constraint and a palette, which represents relationship between quantized colors and gray-levels, is generated. Using the palette, an output monochrome image is obtained. Experiments show that the proposed method obtains good monochrome and rebuilt color images.

In previous work we have proposed a steganographic technique for gray scale images called BPCS-Steganography. We also apply this technique to full color images by decomposing the image into its three color component images and treating each as a gray scale image. This paper proposes a method to apply BPCS-Steganography to palette-based images. In palette-based images, the image data can be decomposed into color component images similar to those of full color images. We can then embed into one or more of the color component images. However, even if only one of the color component images is used for embedding, the number of colors in the palette after embedding can be over the maximum number allowed. In order to represent the image data in palette-based format, color quantization is therefore needed. We cannot change the pixel values of the color component image that contains the embedded information, but can only change the pixel values of the other color component images. We assume that the degrading of the color component2 image with information embedded is smaller than that of the color component images that are used for color reduction. We therefore embed secret information into the G component image, because the human visual system is more sensitive to changes the luminance of a color, and G has the largest contribution to luminance of the three color components. In order to reduce the number of colors, the R and B component images are then changed in a way that minimizes the square error.