矢量量化码书设计及优化算法研究
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摘要
矢量量化作为一种有效的压缩手段，具有编解码简单、压缩比大等特点，使其广泛的应用于卫星遥感影像的压缩，数字电视、DVD等视频的压缩、存储及传输等方面。在矢量量化技术发展的20多年间，逐步形成了三个重点研究方向：码书设计、码字搜索和索引分配，其中码书设计最为关键。虽然矢量量化技术具有许多优点，但在码书设计中由于通常使用的各种局部最优、类全局最优以及全局最优的码书设计算法，使得生成码书的速度不够快、质量不够好。本文针这一制约矢量量化码书设计的关键问题，为加快码书的生成速度及提高码书的质量，分别提出了小生境猴群繁衍模拟算法、自进化萤火虫优化算法，使得码书的生成的速度和质量得到提高。
小生境猴群繁衍模拟算法是以“小生境”思想为基础的遗传码书设计方法。遗传算法作为一种仿生学算法，能够有效的进行全局搜索。本文在传统的基于训练矢量划分的遗传码书设计算法基础上，提出了一种对交叉空间进行一定约束的，基于“小生境”思想的遗传码书设计算法。由于对交叉空间进行了一定的约束，使当前训练矢量总是按照更加优秀的参照码书，以带有一定随机性的并按空间距离最小的方式进行归类，使码书的生成速度得到提高，并在文中给出了此算法的收敛性证明。实验结果表明，此算法在不降低码书质量的基础上，提高了码书的生成速度。
自进化萤火虫算法解决的是提高码书质量的问题。在矢量量化码书设计中，只要当前码书不是全局最优码书，都可以对其进行优化以提高码书质量。矢量量化是一种在高维空间中求解多峰值极值的问题，萤火虫算法在高维空间中求解单峰值极值问题，具有良好的效果，但在高维空间中求解多峰值极问题，该算法不能使用。本文对萤火虫算法在矢量量化码书优化过程中的缺陷进行了细致分析，提出了一种改进算法。通过对震荡系数的动态调整，增大了萤火虫之间的距离、减小引力参数、扩大搜索范围，抑制了算法过快收敛的现象。实验表明，此算法能够提高码书性能0.2---0.45dB，使码书更加优化。
本文将以上两种改进算法应用于图像处理中，并以本文所提出的码书生成算法（或优化算法）为代表，将最终生成的码书分别嵌入到纹理分类及数字水印中。纹理分类以K-View算法作为分类基础框架，将小生境算法生成的码书作为纹理基元参加分类，取得良好效果；在数字水印中，将水印信息直接加载在生成码书中并接受各种攻击。实验结果表明由本文提出的算法生成的码书质量更好，速度更快。
本文对矢量量化码书设计的贡献是针对制约矢量量化技术发展的速度和质量两个瓶颈问题分别给出了解决方案。其中，小生境猴群繁衍模拟算法在遗传算法及小生境技术上提出了一种不依赖于模式定理以及积木块假设的遗传码书快速生成算法，使码书的生成速度大大加快；自进化萤火虫算法在通过对引力参数的控制，实现了对已生成码书的继续优化，使码书质量大大提高
Vector quantization is an effective method of compression. It widely used in satelliteimage and remote sensing image compression because of its simple codec and highcompression rate.It also could be used in compression, storage and transmission of digital TVand DVD video. The development of vector quantization technical is aboult20years, Itgradually formed the three key research direction:codebook design,codeword search andindex assignment.And the codebook design is the most critical of the three. There are avariety of local optimal,near global optimal and global optimal codebook design algorithmbecause we only know about the necessary condition of optimal codebook in theory.It hasprominent defects to restrict this techniqueusing in many areas Althoughvector quantizationtechnology has many advantages. At present, the bottleneck of vector quantization is thespeed of codebook generation is not fast enough and the quality is not good enough orboth.Aiming at the improving generation speed and quality of codebook, two algorithms areproposed respectively"niche monkeys group multiply simulation algorithm" and "selfevolution glowworm swarm optimization algorithm".
Genetic algorithm is a bionics algorithm which could effectively global search. Thearticle propose a new method of genetic algorithm which based on the niche ideas andconstraining the cross space based on the traditional codebook design of genetic algorithm ofdeviding training vector.The training vector could be classification with some randomnessand minimum distance in high-dimensional space based on the better reference codebook.Itcould improve the quality of codebook continuously. It also give the improvement ofconvergence of the algorithm. The experiment also show that the algorithm increase in thegeneration rate of codebook without reducing the quality of codebook.
The self-evolution of firefly algorithm is to improve the codebook quality problems.Inthe codebook design of vector quantization, if the current codebook is not a global optimalcodebook, we could continue optimize it.Vector quantization is a searching extreme problemwith multi-peak of high-dimension. It makes a lot of good algorithms which solve single peakextreme problem well, couldn’t be used in the vector quantization.And firefly algorithm is one of them.The article make a careful analysis of defect in optimization process of codebookusing firefly algorithm. And proposed an improved algorithm.Through the dynamicadjustment of concussion coefficient,it increase the distance of firefly,reduce the parameter ofAttractive and also expand the search area which suppressing the algorithm convergenceexcessive phenomenon. The experiments show that the algorithm can improve the codebookperformance0.2-0.45codebook and more optimized.
In the last chapter,it introduces the usage of codebook in image processing,which taksthe codebook generation algorithm as the representative described in this article.And make thegeneration codebook embedded into texture classification and digital watermarking in thirdchapter.The texture classification using K-view algorithm as a classification framework andalso use generated codebook using niche algorithm as texture primitives in classification.Itachieves good results.In the digital watermark,the codebook embedded in watermarkinformation directly and accept all kinds of attacks,the experimental results show that thegeneration codebook which proposed in chapter third have good quality
The contribution of this paper on the vector quantization codebook design is to solvetwo bottlenecks which restrict the development of vecror quantization technique.Thesimulation algorithm of niche group multiply presents an fast genetic codebook generationalgorithm which does not depend on the schema theorem and building block hypothesis.Thegeneration speed of codebook greatly accelerated.Self evolution firefly algorithm realize thecontinuing optimization of existing codebook through controlling the parameter ofgravity,and also greatly inproves the quality of the codebook.
小生境猴群繁衍模拟算法是以“小生境”思想为基础的遗传码书设计方法。遗传算法作为一种仿生学算法，能够有效的进行全局搜索。本文在传统的基于训练矢量划分的遗传码书设计算法基础上，提出了一种对交叉空间进行一定约束的，基于“小生境”思想的遗传码书设计算法。由于对交叉空间进行了一定的约束，使当前训练矢量总是按照更加优秀的参照码书，以带有一定随机性的并按空间距离最小的方式进行归类，使码书的生成速度得到提高，并在文中给出了此算法的收敛性证明。实验结果表明，此算法在不降低码书质量的基础上，提高了码书的生成速度。
自进化萤火虫算法解决的是提高码书质量的问题。在矢量量化码书设计中，只要当前码书不是全局最优码书，都可以对其进行优化以提高码书质量。矢量量化是一种在高维空间中求解多峰值极值的问题，萤火虫算法在高维空间中求解单峰值极值问题，具有良好的效果，但在高维空间中求解多峰值极问题，该算法不能使用。本文对萤火虫算法在矢量量化码书优化过程中的缺陷进行了细致分析，提出了一种改进算法。通过对震荡系数的动态调整，增大了萤火虫之间的距离、减小引力参数、扩大搜索范围，抑制了算法过快收敛的现象。实验表明，此算法能够提高码书性能0.2---0.45dB，使码书更加优化。
本文将以上两种改进算法应用于图像处理中，并以本文所提出的码书生成算法（或优化算法）为代表，将最终生成的码书分别嵌入到纹理分类及数字水印中。纹理分类以K-View算法作为分类基础框架，将小生境算法生成的码书作为纹理基元参加分类，取得良好效果；在数字水印中，将水印信息直接加载在生成码书中并接受各种攻击。实验结果表明由本文提出的算法生成的码书质量更好，速度更快。
本文对矢量量化码书设计的贡献是针对制约矢量量化技术发展的速度和质量两个瓶颈问题分别给出了解决方案。其中，小生境猴群繁衍模拟算法在遗传算法及小生境技术上提出了一种不依赖于模式定理以及积木块假设的遗传码书快速生成算法，使码书的生成速度大大加快；自进化萤火虫算法在通过对引力参数的控制，实现了对已生成码书的继续优化，使码书质量大大提高
Vector quantization is an effective method of compression. It widely used in satelliteimage and remote sensing image compression because of its simple codec and highcompression rate.It also could be used in compression, storage and transmission of digital TVand DVD video. The development of vector quantization technical is aboult20years, Itgradually formed the three key research direction:codebook design,codeword search andindex assignment.And the codebook design is the most critical of the three. There are avariety of local optimal,near global optimal and global optimal codebook design algorithmbecause we only know about the necessary condition of optimal codebook in theory.It hasprominent defects to restrict this techniqueusing in many areas Althoughvector quantizationtechnology has many advantages. At present, the bottleneck of vector quantization is thespeed of codebook generation is not fast enough and the quality is not good enough orboth.Aiming at the improving generation speed and quality of codebook, two algorithms areproposed respectively"niche monkeys group multiply simulation algorithm" and "selfevolution glowworm swarm optimization algorithm".
Genetic algorithm is a bionics algorithm which could effectively global search. Thearticle propose a new method of genetic algorithm which based on the niche ideas andconstraining the cross space based on the traditional codebook design of genetic algorithm ofdeviding training vector.The training vector could be classification with some randomnessand minimum distance in high-dimensional space based on the better reference codebook.Itcould improve the quality of codebook continuously. It also give the improvement ofconvergence of the algorithm. The experiment also show that the algorithm increase in thegeneration rate of codebook without reducing the quality of codebook.
The self-evolution of firefly algorithm is to improve the codebook quality problems.Inthe codebook design of vector quantization, if the current codebook is not a global optimalcodebook, we could continue optimize it.Vector quantization is a searching extreme problemwith multi-peak of high-dimension. It makes a lot of good algorithms which solve single peakextreme problem well, couldn’t be used in the vector quantization.And firefly algorithm is one of them.The article make a careful analysis of defect in optimization process of codebookusing firefly algorithm. And proposed an improved algorithm.Through the dynamicadjustment of concussion coefficient,it increase the distance of firefly,reduce the parameter ofAttractive and also expand the search area which suppressing the algorithm convergenceexcessive phenomenon. The experiments show that the algorithm can improve the codebookperformance0.2-0.45codebook and more optimized.
In the last chapter,it introduces the usage of codebook in image processing,which taksthe codebook generation algorithm as the representative described in this article.And make thegeneration codebook embedded into texture classification and digital watermarking in thirdchapter.The texture classification using K-view algorithm as a classification framework andalso use generated codebook using niche algorithm as texture primitives in classification.Itachieves good results.In the digital watermark,the codebook embedded in watermarkinformation directly and accept all kinds of attacks,the experimental results show that thegeneration codebook which proposed in chapter third have good quality
The contribution of this paper on the vector quantization codebook design is to solvetwo bottlenecks which restrict the development of vecror quantization technique.Thesimulation algorithm of niche group multiply presents an fast genetic codebook generationalgorithm which does not depend on the schema theorem and building block hypothesis.Thegeneration speed of codebook greatly accelerated.Self evolution firefly algorithm realize thecontinuing optimization of existing codebook through controlling the parameter ofgravity,and also greatly inproves the quality of the codebook.
引文
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