[1]姜来,黄彩玲,纪震.基于粒子群优化算法的矢量量化图像压缩方法[J].深圳大学学报理工版,2006,23(3):268-271.
 JIANG Lai,HUANG Cai-ling,and Ji Zhen.A new PSO-based image compression method[J].Journal of Shenzhen University Science and Engineering,2006,23(3):268-271.
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基于粒子群优化算法的矢量量化图像压缩方法()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第23卷
期数:
2006年3期
页码:
268-271
栏目:
生命科学
出版日期:
2006-07-30

文章信息/Info

Title:
A new PSO-based image compression method
文章编号:
1000-2618(2006)03-0268-04
作者:
姜来黄彩玲纪震
深圳大学信息工程学院,深圳518060
Author(s):
JIANG Lai HUANG Cai-ling and Ji Zhen
College of Information Engineering Shenzhen University
关键词:
粒子群优化算法码书设计矢量量化LBG算法图像压缩
Keywords:
particle swarm optimization codebook design vector quantization LBG image compression
分类号:
TP 301.6
文献标志码:
A
摘要:
提出一种基于粒子群优化算法的图像矢量量化码书设计算法.该算法引入粒子群的全局搜索策略,结合矢量量化码书设计方法,增加了算法解的随机性和多样性.实验结果显示,本算法与传统LBG码书设计算法相比,具有更强的鲁棒性,可有效解决LBG算法对初始码书的依赖性,能获得性能较好的码书.
Abstract:
This paper presents a new method toward the design of optimized codebooks by vector quantization (VQ). VQ is a theoretically optimal coding technique that has been widely used as a powerful data compression technique. The conventional VQ technique (e.g. ,LBG algorithm)is easy to converge in a local optimum codebook, which is near to the initial codebook because only the attraction of each training vector and code vector is considered in these techniques. A strategy of particle swarm optimization(PSO) is proposed to improve the conventional LBG algorithm. PSO is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. PSO can solve a variety of hard optimization problems but it has a faster convergence rate and it has very few parameters to adjust which makes it particularly easy to implement. In this paper, the fundamental principle and algorithm of PSO and VQ are introduced. The feasibility of PSO for LBG codebook design and the LBG-based image compression method using VQ are studied. The experiment results show that this proposed method is more effective in codebook design in comparison with the conventional LBG algorithm.

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更新日期/Last Update: 2015-06-26