# An Efficient Palette Generation Method for Color Image Quantization
## 1 Introduction  
Nowadays, RGB color images are widely used for storage, transmission and display. In order to reduce the storage space required and the transfer time of the color images, several color image quantization techniques have been proposed [1]. Color image quantization consists of two procedures. The first is to design a color palette which is a set of representative colors, while the second is to map each image pixel to one color in the color palette.  
The key to color image quantization is a good color palette. If a good color palette is used, good reconstructed image quality of the compressed color image can be achieved. Several palette design methods have been proposed for color image quantization. [PERSON] and [PERSON] [2] proposed the binary splitting approach for color image quantization. [PERSON] [3] employed an iterative process to divide the color space into boxes based on variance minimization. Then, the average value of each resulting box is represented as a color of the palette. [PERSON] and [PERSON] [4] proposed a color quantization technique which is based on the Kohonen neural network to train the color images. [PERSON] and [PERSON] [5] calculated the sorted histogram list, and an adaptive clustering algorithm used this list to extract the palette colors for color image quantization. [PERSON] et al. [6] developed a color image quantization algorithm that combines particle swarm optimization with the K-means clustering algorithm. [PERSON] et al. [7] proposed the ant colony algorithm for color reduction. [PERSON] and [PERSON] [8] employed two test conditions to accelerate the K-means algorithm for color image quantization. [PERSON] et al. [9] proposed two schemes to design the color palette. The first scheme employed the splitting-based technique for initial palette generation, and the second employed the k-means algorithm to generate the final color palette. [PERSON] [10] introduced fast variants of k-means with several initialization schemes for color image quantization. [PERSON] et al. [11] introduced an effective color quantizationmethod which is based on divisive hierarchical clustering and the binary splitting strategy. [PERSON] [12] proposed an optimized Fuzzy C-means algorithm for color image quantization. [PERSON] [13] proposed a method which makes a contribution regarding the use of image quantization to dimensionality reduction of visual data. [PERSON] et al. [14] described a color quantization method using statistical features obtained by linear discriminant analysis and principal component analysis. [PERSON] et al. [15] proposed a fast color image quantization method based on K-means clustering combined with image sampling. [PERSON] [16] employed the shuffled-frog leaping algorithm for color image quantization. [PERSON] [17] solved the color quantization problem by combining the particle swarm optimization algorithm with the Ant-tree.  
Although these methods can generate quantized images, some methods generate poorer images and some consume a great deal of time. The aim of this study is to generate high-quality quantized images with low computational cost. Experimental results show that high-quality quantized images can be generated by the proposed method. When the sampling rate equals 0.125, the computation time of the proposed method is less than 0.3 s for all cases.  
The rest of this paper is organized as follows. Section 2 presents the related work. Section 3 gives a description of the color image quantization method. In Section 4, the experimental results and discussion are presented. Section 5 gives the conclusions.