北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2007, Vol. 30 ›› Issue (4): 64-68.doi: 10.13190/jbupt.200704.64.wangyy

• 论文 • 上一篇    下一篇

有效图像压缩的提升小波优化设计

王永玉,孙衢,袁超伟   

  1. (1.北京邮电大学 通信网络综合技术研究所, 北京 100876; 2.四川大学 电气信息学院, 成都 610065)
  • 收稿日期:2006-07-07 修回日期:2007-03-07 出版日期:2007-08-30 发布日期:2007-08-30
  • 通讯作者: 袁超伟

Optimal Design of Wavelets via Lifting for Effective Image Compression

WANG Yong-yu1,SUN Qu-2,YUAN Chao-wei1   

  1. (1 School of Telecommunication and Network Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; 2 School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China)
  • Received:2006-07-07 Revised:2007-03-07 Online:2007-08-30 Published:2007-08-30
  • Contact: WANG Yong-yu

摘要:

提出了多种群遗传算法和蚂蚁算法融合的提升格式小波优化设计方法。首先采用多种群遗传算法对构成小波的提升步进行优化;并提出局部适应度和全局适应度的概念,将其和蚂蚁算法中蚂蚁选择路径时对全局启发信息和局部启发信息的利用有机地结合起来;采用蚂蚁算法对遗传优化的结果进一步寻优,精确求解适合给定变换问题的最优小波,形成一种时间效率和求解效率都比较好的启发式随机优化方法。将所设计的小波应用于基于小波的图像编码器对指纹及医学图像压缩,实验结果验证了设计方法的有效性和图像压缩性能的优越性。

关键词: 小波, 提升方法, 遗传算法, 蚂蚁算法, 图像压缩

Abstract:

Lifting scheme wavelet design based on multi-population genetic algorithm (GA) and ant system (AS) for effective image compression is proposed. Firstly, a multi-population genetic algorithm and lifting are used to evolve wavelets. The global fitness and the local fitness are introduced in GA for the first time and they are homologous to the global elicitation information and the local one utilized by ants in AS. So the AS algorithm is then used to find the exact wavelet that is adapted best to the given application of the wavelet transform, and the resulting stochastic optimization method with elicitation is good both in time efficiency and with accurate solution. The wavelets designed are applied to wavelet-based image coders for compression of fingerprint and medical image, and the experiment results validate the effectiveness of the design method and the advantage of the resulting image compression.

Key words: wavelet, lifting scheme, genetic algorithm, ant system, image compression

中图分类号: