A new technique for color quantization is suggested. First, pre-quantization is accomplished by means of spatial resolution reduction; then, color aggregation is accomplished based on the distance between colors in the color space. Color aggregation is an iterated process where the number of iterations is given by the difference between the number of colors of the pre-quantized image, and the number of colors desired for the quantized image. Color mapping is finally accomplished. Performance evaluation is done in terms of generally adopted quality measures. Comparisons with other methods in the literature are also provided.

A new technique is presented for color image segmentation. Five processes are accomplished that are respectively dealing with color image quantization, noisy regions removal, removal of thin regions, color-based region merging, and area-based region merging. Some parameters involved in the method are automatically computed, others are fixed depending on the specific application. Thus, the method is characterized by some flexibility that makes it useful for different applications. The method has been checked on color images from publicly available repositories. The performance of the method has been evaluated in terms of Precision, Recall and F-measure. The obtained results are satisfactory from both a qualitative and a quantitative point of view.

A technique for color quantization is described, which consists of two processes. The first process is based on the analysis of the histograms of the three color components of the RGB input image. The second process performs clustering of the colors quantized by the first process, based on their Euclidean distance. At the end of the second process, the output image is obtained by replacing the color of each pixel of the input image with the closest representative color. The obtained results are satisfactory from both the qualitative and the quantitative point of view.

We present a color image segmentation algorithm, RCRM, based on the detection of Representative Colors and on Region Merging. The 3D color histogram of the RGB input image is built. Colors are processed in decreasing frequency order and a grouping process is accomplished to gather in the same cluster all colors that are close enough to the current color. Colormapping is done to originate a preliminary image segmentation. Segmentation regions having small size undergo a merging process. Merging is actually accomplished only for adjacent regions whose colors do not significantly differ. The parameters involved by the algorithm are set automatically by taking into account color distribution in the input image and geometrical features of the regions into which the image is partitioned. The algorithm has been tested on a large number of RGB color images originating satisfactory results.

A new color quantization algorithm, CQ, is presented, which includes two phases. The first phase reduces the number of colors by reducing the spatial resolution of the input image. The second phase furthermore reduces the number of colors by performing color clustering guided by distance information. Then, color mapping completes the process. The algorithm has been tested on a large number of color images with different size and color distribution, and the performance has been compared to the performance of other algorithms in the literature.

A color quantization algorithm is presented, which is based on the reduction of the spatial resolution of the input image. The maximum number of colors nf desired for the output image is used to fix the proper spatial resolution reduction factor. This is used to build a lower resolution version of the input image with size nf. Colors found in the lower resolution image constitute the palette for the output image. The three components of each color of the palette are interpreted as the coordinates of a voxel in the 3D discrete space. The Voronoi Diagram of the set of voxels corresponding to the colors of the palette is computed and is used for color mapping of the input image.