An Efficient Palette Generation Method for Color Image Quantization

Shu-Chien Huang

doi:10.3390/app11031043

Abstract

This article describes an efficient method to generate a color palette for color image quantization. The method consists of two stages. In the first stage, the initial palette is generated. Initially, the color palette is an empty set. First, the N colors are generated according to the data distribution of the input image in the RGB (Red, Green, Blue) color space. Then, one color is selected from the N colors and this color is added to the initial palette, and the step is repeated until the color number of the initial palette is equal to K. In the second stage, the quantized image is generated using the fast K-means algorithm. There are many sampling rates used in this study. For each sampled pixel, a fast searching method is employed to efficiently determine the closest color in the palette. Experimental results show that the 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.

Highlights

Nowadays, RGB color images are widely used for storage, transmission and display
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

Summary

Introduction

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]. Several palette design methods have been proposed for color image quantization. Omran et al [6] developed a color image quantization algorithm that combines particle swarm optimization with the K-means clustering algorithm. Hu and Su [8] employed two test conditions to accelerate the K-means algorithm for color image quantization. Celebi [10] introduced fast variants of k-means with several initialization schemes for color image quantization. Celebi et al [11] introduced an effective color quantization method which is based on divisive hierarchical clustering and the binary splitting strategy. El-Said [12] proposed an optimized Fuzzy C-means algorithm for color image quantization. Frackiewicz et al [15] proposed a fast color image quantization method based on K-means clustering combined with image sampling. Pérez-Delgado [17] solved the color quantization problem by combining the particle swarm optimization algorithm with the Ant-tree

Objectives

Results

Conclusion