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

doi:10.13190/jbupt.201101.69.wuyq

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2011, Vol. 34 ›› Issue (1): 69-74.doi: 10.13190/jbupt.201101.69.wuyq

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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

Abstract

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

References

[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.

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

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