Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (9): 2646-2651.DOI: 10.11772/j.issn.1001-9081.2019030384

• Advanced computing • Previous Articles     Next Articles

Method for solving color images quantization problem of color images

LI He, JIANG Dengying, HUANG Zhangcan, WANG Zhanzhan   

  1. School of Science, Wuhan University of Technology, Wuhan Hubei 430073, China
  • Received:2019-03-11 Revised:2019-04-15 Online:2019-09-10 Published:2019-04-28
  • Supported by:

    This work is partially supported by the Fundamental Research Funds for the Central Universities (2017IB014), the Humanities and Social Sciences Research Project of Ministry of Education of the People's Republic of China (13YJCZH060).


李贺, 江登英, 黄樟灿, 王占占   

  1. 武汉理工大学 理学院, 武汉 430073
  • 通讯作者: 江登英
  • 作者简介:李贺(1994-),女,河南驻马店人,硕士研究生,主要研究方向:应用数学、图像处理;江登英(1976-),女,湖北襄阳人,教授,博士,主要研究方向:应用数学;黄樟灿(1960-),男,浙江绍兴人,教授,博士,主要研究方向:智能计算、图像处理;王占占(1993-),男,安徽淮北人,硕士研究生,主要研究方向:智能计算。
  • 基金资助:



For the color quantization problem of color images, the K-means clustering algorithm has strong dependence on initial conditions and is easy to fall into local optimum, and the traditional intelligent optimization algorithms only consider the mutual competition between individuals in the population layer and ignores the mutual cooperation between the population layers. To solve the problems, a K-means-based PES (Pyramid Evolution Strategy) color image quantization algorithm was proposed. Firstly, the clustering loss function in K-means clustering algorithm was used as the fitness function of the new algorithm; secondly, PES was used for the population initialization, layering, exploration, acceleration and clustering of the colors; finally, the new algorithm was used to quantify four standard color test images at different color quantization levels. The experimental results show that the proposed algorithm can improve the defects of the K-means clustering algorithm and the traditional intelligent algorithm. Under the criterion of intra-class mean squared error, the average distortion rate of the image quantized by the new algorithm is 12.25% lower than that quantized by the PES-based algorithm, 15.52% lower than that quantized by the differential evolution algorithm, 58.33% lower than that quantized by the Particle Swarm Optimization (PSO) algorithm, 15.06% lower than that quantized by the K-means algorithm; and the less the color quantization levels, the more the image distortion rate reduced quantized by the new algorithm than that quantized by other algorithms. In addition, the visual effect of the image quantized by the proposed algorithm is better than that quantized by other algorithms.

Key words: image quantization, pyramid structure, K-means clustering, Particle Swarm Optimization (PSO) algorithm, intelligent algorithm



关键词: 图像量化, 金字塔结构, K均值聚类, 粒子群优化算法, 智能算法

CLC Number: