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.
李贺, 江登英, 黄樟灿, 王占占. 彩色图像颜色量化问题的求解方法[J]. 计算机应用, 2019, 39(9): 2646-2651.
LI He, JIANG Dengying, HUANG Zhangcan, WANG Zhanzhan. Method for solving color images quantization problem of color images. Journal of Computer Applications, 2019, 39(9): 2646-2651.
[1] 徐速,胡健,周元.基于微粒群优化算法的颜色量化[J].数字通信,2011,38(2):61-63.(XU S, HU J, ZHOU Y. Color quantization based on particle swarm optimization algorithm[J]. Digital Communication, 2011, 38(2):61-63.)
[2] 谢江宁,王磊.基于层级分割的颜色恒常性算法[J].山东大学学报(理学版),2016,51(1):101-105.(XIE J N, WANG L. Color constancy using hierarchical segments[J]. Journal of Shandong University (Natural Science), 2016, 51(1):101-105.)
[3] 耿国华,周明全.基于八叉树结构的色彩量化算法[J].小型微型计算机系统,1997,18(1):24-29.(GENG G H, ZHOU M Q. Color quantization algorithm based on octree structure[J]. Mini-Micro Systems, 1997, 18(1):24-29.)
[4] FRACKIEWICZ M, PALUS H. K-means color image quantization with deterministic initialization:new image quality metrics[C]//Proceedings of the 2018 International Conference on Image Analysis and Recognition, LNCS 10882. Berlin:Springer, 2018:56-61.
[5] ZHANG Y, XU T, GAO W. Image retrieval based on GA integrated color vector quantization and curvelettransform[C]//Proceedings of the 2012 International Conference in Swarm Intelligence, LNCS 7331. Berlin:Springer, 2012:406-413.
[6] 沙秋夫,刘向东,何希勤,等.一种基于粒子群算法的色彩量化方案[J].中国图象图形学报,2007,12(9):1544-1548.(SHA Q F, LIU X D, HE X Q, et al. Color image quantization using particle swarm optimization[J]. Journal of Image and Graphics, 2007, 12(9):1544-1548.)
[7] 许永峰,姜振益.一种基于粒子群优化的K-均值彩色图像量化算法[J].西北大学学报(自然科学版),2012,42(3):351-354.(XU Y F, JIANG Z Y. A K-means color image quantization method based on particle swarm optimization[J]. Journal of Northwest University (Natural Science Edition), 2012, 42(3):351-354.)
[8] ALAMDAR F, BAHMANI Z, HARATIZADEH S. Color quantization with clustering by F-PSO-GA[C]//Proceedings of the 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems. Piscataway, NJ:IEEE, 2010:233-238.
[9] OMRAN M G, ENGELBRECHT A P, SALMAN A. A color image quantization algorithm based on particle swarm optimization[J]. Informatica, 2005, 29(3):261-270.
[10] 苏清华.图像颜色量化模型优化方法及其在裂纹图像中的应用[D].武汉:武汉理工大学,2013:11-57.(SU Q H. Optimization methods of image color quantization model and its application in crack images[D]. Wuhan:Wuhan University of Technology, 2013:11-57.)
[11] FREISLEBEN B, SCHRADER A. An evolutionary approach to color image quantization[C]//Proceedings of the 1997 IEEE International Conference on Evolutionary Computation. Piscataway, NJ:IEEE, 1997:459-464.
[12] 谈庆.基于金字塔结构的群智能演化策略[D].武汉:武汉理工大学,2018:13-21.(TAN Q. Group intelligence evolution strategy based on pyramid structure[D]. Wuhan:Wuhan University of Technology, 2008:13-21.)
[13] DURKHEIM É. The Division of Labour in Society[M]. London:Macmillan Education UK, 2013:5-10.