免疫粒子群核模糊聚类快速分形图像编码

doi:10.13190/jbupt.201101.69.wuyq

北京邮电大学学报 ›› 2011, Vol. 34 ›› Issue (1): 69-74.doi: 10.13190/jbupt.201101.69.wuyq

免疫粒子群核模糊聚类快速分形图像编码

吴一全，孙子翼

南京航空航天大学信息科学与技术学院，南京 210016

收稿日期:2010-04-14 修回日期:2010-09-12 出版日期:2011-02-28 发布日期:2011-02-28
通讯作者: 吴一全 E-mail:nuaaimage@yahoo.com.cn

Fast Fractal Image Coding Based on Immunity Particle Swarm Optimization and Fuzzy Kernel Clustering

Received:2010-04-14 Revised:2010-09-12 Online:2011-02-28 Published:2011-02-28
Contact: Wu Yi-Quan E-mail:nuaaimage@yahoo.com.cn
Supported by:
National Natural Science Foundation of China

摘要/Abstract

摘要：

针对经典分形编码算法编码时间过长和基于K均值聚类等快速分形编码算法依赖数据分布等问题，提出了一种基于免疫粒子群优化（IPSO）和核模糊聚类的快速分形图像编码算法. 提出基于IPSO的核模糊聚类算法，将IPSO算法应用于聚类中心的求解中，并将其应用于分形图像编码，分别对子块和父块进行核模糊聚类，以更加合理的分类搜索取代全局搜索，减少编码时间. 实验结果表明，新算法的编码时间约为经典分形编码算法的1/6，其峰值信噪比只略微下降；与基于K均值聚类和基于粒子群优化聚类等快速分形图像编码算法相比，新算法能以更少的编码时间获得更高的峰值信噪比.

关键词: 图像编码, 快速分形编码, 核模糊聚类, 免疫粒子群优化

Abstract:

Aiming at the lengthy of classical fractal coding algorithm and the problem of fast fractal image coding algorithm based on such as Kmeans clustering, a fast fractal image coding algorithm based on immunity particle swarm optimization（IPSO）and fuzzy kernel clustering is proposed. Firstly, an algorithm of fuzzy kernel clustering based on IPSO is presented. The IPSO algorithm is used to calculate the cluster centers. Then the proposed algorithm of fuzzy kernel clustering based on IPSO is applied to fractal image coding. The range blocks and domain blocks are clustered reasonably by fuzzy kernel method, respectively. Range blocks are searched in the corresponding category of domain blocks. As a result, the encoding time is reduced significantly. The experimental results show that, the encoding time of the proposed algorithm is about six times less than that of the classical algorithm at the cost of slight decrease of peak signaltonoise ratio. Compared with the fast fractal image coding algorithm reported recently based on such as Kmeans clustering and particleswarmoptimization clustering, the proposed algorithm can achieve higher peak signaltonoise ratio in much less encoding time.

Key words: image coding, fast fractal coding, fuzzy kernel clustering, immunity particle swarm optimization

吴一全，孙子翼. 免疫粒子群核模糊聚类快速分形图像编码[J]. 北京邮电大学学报, 2011, 34(1): 69-74.

参考文献

[1] Jacquin A E. Image coding based on a fractal theory of iterated contractive image transformations [J]. IEEE Trans on Image Processing, 1992, 1(1): 18-30. [2] Lai C M, Lam K M, Siu W C. A fast fractal image coding based on kick-out and zero contrast condition[J]. IEEE Trans on Image Processing, 2003, 12(11): 1398-1403. [3] Distasi R, Nappi M, Riccio D. A range/domain approximation error-based approach for fractal image compression[J]. IEEE Trans on Image Processing, 2006, 15(1): 89-97. [4] Iano Y, da Silva F S, Cruz A L M. A fast and efficient hybrid fractal-wavelet image coder[J]. IEEE Trans on Image Processing, 2006, 15(1): 98-105. [5] 王向阳, 于雁春. 基于改进K-均值聚类的快速分形图像编码算法[J]. 计算机科学, 2008, 35(2): 219-222. Wang Xiangyang, Yu Yanchun. A fast fractal image compression using the improved K-mean clustering[J]. Computer Science, 2008, 35(2): 219-222. [6] 杨红颖, 王向阳, 于雁春. 基于FCM聚类的快速分形图像编码算法[J]. 小型微型计算机系统, 2009, 30(3): 493-497. Yang Hongying, Wang Xiangyang, Yu Yanchun. Fast fractal image compression based on fuzzy C-means clustering[J]. Journal of Chinese Computer System, 2009, 30(3): 493-497. [7] Belloulata K. Fast fractal coding of subbands using a non-iterative block clustering [J]. Journal of Visual Communication and Image Representation, 2005, 16(1): 55-67. [8] 陈作平, 叶正麟, 郑红婵, 等. 基于K-均值聚类的快速分形编码方法[J]. 中国图像图形学报, 2007, 12(4): 586-591. Chen Zuoping, Ye Zhenglin, Zheng Hongchan, et al. Fast fractal coding technique based on K-mean clustering[J]. Journal of Image and Grahphics, 2007, 12(4): 586-591. [9] Mitra S K, Murtyh C A, Kundu M K. Technique for fractal image compression using genetic algorithm[J]. IEEE Trans on Image Processing, 1998, 7(4): 586-593. [10] 周晨光, 邱祖廉. 一种改进的紧凑遗传算法及其在分形图像压缩中的应用[J]. 中国图像图形学报, 2007, 12(4): 597-602. Zhou Chenguang, Qiu Zulian. An improved compact genetic algorithm with application in fractal image compression[J]. Journal of Image and Graphics, 2007, 12(4): 597-602. [11] 申元霞. 自主式粒子群优化模型研究[J]. 重庆邮电大学学报: 自然科学版, 2009, 21(4): 507-511. Shen Yuanxia. Study on self regulated model of particle swarm optimization[J]. Journal of Chongqing University of Posts and Telecommunications: Natural Science Edition, 2009, 21(4): 507-511. [12] Tseng C C, Hsieh J G, Jeng J H. Fractal image compression using visual-based particle swarm optimization[J]. Image and Vision Computing, 2008, 26: 1154-1162. [13] Kim D W, Lee K Y, Lee D, et al. Evaluation of performance of clustering algorithm in kernel-induced feature space[J]. Pattern Recogntion, 2005, 38(4): 607-611. [14] 高鹰, 谢胜利. 免疫粒子群优化算法[J]. 计算机工程与应用, 2004(6): 4-6, 33. Gao Ying, Xie Shengli. Particle swarm optimization algorithms with immunity[J]. Computer Engineering and Application, 2004(6): 4-6, 33. [15] 郑晓鸣, 吕士颖, 王晓东. 免疫接种粒子群的聚类算法[J]. 电子科技大学学报, 2007, 36(6): 1264-1267. Zheng Xiaoming, Lü Shiying, Wang Xiaodong. Clustering with immunity-vaccination based on particle swarm optimization algorithm[J]. Journal of University of Electronic Science and Technology of China, 2007, 36(6): 1264-1267.

免疫粒子群核模糊聚类快速分形图像编码

Fast Fractal Image Coding Based on Immunity Particle Swarm Optimization and Fuzzy Kernel Clustering

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