Gene Expression Programming Applied to Fractals Image Compression Encoding

LI Wen-jing; LIU Zhi-jia; LIAO Wei-zhi

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Gene Expression Programming Applied to Fractals Image Compression Encoding

更新时间：2026-09-04

- Gene Expression Programming Applied to Fractals Image Compression Encoding
- Vol. 28, Issue 8, Pages: 67-69(2011)
- 作者机构：
  
  广西师范学院机构计算机与信息工程学院,广西,南宁,530001
- 作者简介：
- 基金信息：
- DOI：
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- Published：2011
- 稿件说明：
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李文敬, 刘之家, 廖伟志. 基因表达式编程在分形图像压缩编码中的应用[J]. 微电子学与计算机, 2011, 28(8): 67-69,73.

LI Wen-jing, LIU Zhi-jia, LIAO Wei-zhi. Gene Expression Programming Applied to Fractals Image Compression Encoding[J]. 2011, 28(8): 67-69.
李文敬, 刘之家, 廖伟志. 基因表达式编程在分形图像压缩编码中的应用[J]. 微电子学与计算机, 2011, 28(8): 67-69,73. DOI：

LI Wen-jing, LIU Zhi-jia, LIAO Wei-zhi. Gene Expression Programming Applied to Fractals Image Compression Encoding[J]. 2011, 28(8): 67-69. DOI：

摘要

针对目前存在图像压缩率不高、全局搜索或遗传算法寻找最优分形图像压缩编码速度慢的不足

将基因表达式编程应用于分形图像压缩编码

提高求解分形图像压缩编码速度和压缩比.首先从理论上对二值图像压缩编码的求解过程和基因表达式编程在压缩编码中的作用机理进行分析;然后

研究分形图像压缩编码的基因和染色体的表示方法

适应度函数设计以及选择、变异、插串、基因变换、基因重组等基因遗传进化操作过程.提出基因表达式编程的分形图像压缩算法

求解分形图像压缩编码的最优解.实验结果表明

基因表达式编程应用于分形图像压缩编码

具有较强的全局寻优能力

搜索最优解的速度比遗传算法快约2倍

图像压缩率高.

Abstract

Based on the existing image compression rate was not high and global search or genetic algorithm to find the optimal fractal image compression coding deficiencies slow at present.Gene expression programming applied to fractals image compression encoding

so that improve the speed and compression ratio of fractal image compression.First

binary fractal image compression process of solving IFS was analyzed;the gene and chromosome structure and coding of fractal image compression IFS of gene expression programming were given.According to the similarity of fractal images

compression and shrinkage characteristics

and the fitness function of gene expression programming and genetic evolution operating of select

mutating

insert string

gene transformation

gene recombination were given.The fractals image compression algorithm based on genetic expression programming was put forward.The experimental results show that the algorithm has a fairly strong global search optimization capacity

convergence rate of about twice times faster than based on genetic algorithm and high compression ratio.

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