小波图像分类矢量量化与网络编码量化的研究
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摘要
本文在分析了图像小波变换的原理和子带系数空间及频率分布特点的基础上，充分利用标量量化、矢量量化、网格编码量化、网格编码矢量量化、矢量分类、码书扩展和基于人眼视觉特性的加权均方误差准则等思想和方法，从信息融合的不同角度展开了对小波图像的压缩编码研究，同时也讨论了这些方法在静止图像量化中的具体应用。通过对作者提出的多种压缩编码算法原理的分析、算法实现方案的设计和大量的仿真实验，表明了其创新性和有效性。
第二章首先详细介绍了图像小波分析的基本理论，其中包括从小波多分辨率分析理论开始到离散小波变换再到图像的二维离散小波变换即MALAT算法，并着重分析了图像小波变换系数的空间和频率分布特点。在此基础上，结合小波零树数据结构和最优量化的方法提出了一种快速零树编码方法。对快速零树编码算法作了原理分析，设计了具体的实现方案，给出了仿真结果和分析。
第三章详细介绍了矢量量化在图像小波变换域的应用，给出了三种有效的编码方案。首先回顾了矢量量化的基本原理和一般算法，对小波变换结合矢量量化技术的特点作出分析和总结。在此基础上，结合零树编码思想，以及基于人眼视觉特性和分类矢量量化的思想，根据不同的矢量构成和分类方法提出了三种混合型静止图像编码方法，给出了算法原理的分析、实现方案和仿真结果，最后对本章方法作出分析总结。
第四章是在第二章标量量化的基础上引入网格编码量化的方法以进一步提高量化增益。由于TCQ的基本思想源于TCM，较好地掌握TCM的思想有助于深刻理解TCQ，所以首先介绍了网格编码调制；维特比算法是TCM和TCQ提高编码增益的关键，文中对TCM和TCQ都要用到的卷积编码和维特比译码也作了详细的介绍。在此基础上重点介绍了TCQ原理，并对TCQ的性能进行了分析。紧接着提出了小波图像TCQ算法，分析了其原理和TCQ算法实现。最后给出了仿真结果和分析。
第五章是上一章介绍的TCQ方法由标量空间向矢量空间的直接拓展。首先分析了网格编码矢量量化的原理，在第三章矢量量化的基础上详细介绍了小波图像分类加权TCVQ算法的原理和实现过程，并给出了TCVQ在小波图像量化中的应用实例和仿真结果。最后对本章算法作出总结分析。
第六章在TCVQ基础上运用小码书扩展和块编码的思想方法提出在二维码
电子科技大学搏士论文
书空间进行量化的方案，即二维 TCVQ。设计了二维 TCVQ应用于图像编码量化
的算法，给出了二维TCVQ在图像空域的仿真结果和分析。
Based on the analysis of image wavelet transformation and the space/frequency distributing characteristics of different subbands' coefficients, this dissertation fully exploits the following theories and methods: scalar quantization, vector quantization, trellis coded quantization, trellis coded vector quantization, vector classification, codebook expansion and weighted mean square error rule basing mankind visual characteristics,etc. From different angles of information amalgamation, It develops several innovative algorithms of image compression and coding, gives their realization schemes, and makes plentiful simulation tests. The results show their efficiency and validity.
Chapter 2 first reviews the basic theory concerned with image wavelet transformation, which includes the wavelet multi-resolution analysis theory, the discrete wavelet transformation and the two dimension discrete wavelet transformation (Mallat algorithm), and analyzes the space and frequency distributing characteristics of image wavelet coefficients. Then it proposes a new fast zerotree encoded algorithm combing wavelet image zerotree data-structure and optimum quantization, designs a realization scheme and gives simulation results and their analysis.
Chapter 3 particularly introduces vector quantization and its applications to wavelet image quantization. It gives three effective coding schemes, firstly it reviews the fundamental theory of vector quantization and its current algorithms, then it analyzes and summarizes characteristics of wavelet image quantization with VQ. Three commixed still image coding algorithms are proposed based on them and such ideas as: zerotree coding, WMSE (which is based on mankind visual characteristics), classified vector quantization with different vector
structures and classification methods. The chapter gives the principium analysis, realization schemes and simulation results of the proposed algorithms. At last, conclusions are drawn on these algorithms.
Based on scalar quantization of chapter 2,Chapter 4 introduces trellis coded quantization to improve quantization gain. Since the fundamental idea of TCQ originates from trellis coded modulation, in order to comprehend TCQ, it firstly introduces TCM. Also, because Viterbi decoding algorithm is the key of coding gain of TCM and TCQ, it particularly introduces convolutional coding and Viterbi decoding algorithm. Based on them it emphatically describes TCQ principium and analyzes its performance. Following on the heels of these it proposes wavelet image TCQ algorithm, and analyzes its principium and its TCQ algorithm realization. Finally, it gives simulation results and its analysis.
Chapter 5 is the direct extension of chapter 2, namely it maps SQ field to VQ field. This chapter first introduces the principium of trellis coded vector quantization, then focuses on analyzing the algorithm of wavelet image classified weighted TCVQ and its realization process, also gives TCVQ application to wavelet image coding and its simulation results. Finally, it draws conclusions to the algorithms of this chapter.
Using TCVQ and ideas of small codebook expansion and block coding, Chapter 6 proposes a new quantization precept making quantization on two dimension codebook space, that is 2DJTCVQ. It designs an algorithm applying 2DJTCVQ to image quantization, also gives analysis and simulation results of 2D-TCVQ in image space domain.
第二章首先详细介绍了图像小波分析的基本理论，其中包括从小波多分辨率分析理论开始到离散小波变换再到图像的二维离散小波变换即MALAT算法，并着重分析了图像小波变换系数的空间和频率分布特点。在此基础上，结合小波零树数据结构和最优量化的方法提出了一种快速零树编码方法。对快速零树编码算法作了原理分析，设计了具体的实现方案，给出了仿真结果和分析。
第三章详细介绍了矢量量化在图像小波变换域的应用，给出了三种有效的编码方案。首先回顾了矢量量化的基本原理和一般算法，对小波变换结合矢量量化技术的特点作出分析和总结。在此基础上，结合零树编码思想，以及基于人眼视觉特性和分类矢量量化的思想，根据不同的矢量构成和分类方法提出了三种混合型静止图像编码方法，给出了算法原理的分析、实现方案和仿真结果，最后对本章方法作出分析总结。
第四章是在第二章标量量化的基础上引入网格编码量化的方法以进一步提高量化增益。由于TCQ的基本思想源于TCM，较好地掌握TCM的思想有助于深刻理解TCQ，所以首先介绍了网格编码调制；维特比算法是TCM和TCQ提高编码增益的关键，文中对TCM和TCQ都要用到的卷积编码和维特比译码也作了详细的介绍。在此基础上重点介绍了TCQ原理，并对TCQ的性能进行了分析。紧接着提出了小波图像TCQ算法，分析了其原理和TCQ算法实现。最后给出了仿真结果和分析。
第五章是上一章介绍的TCQ方法由标量空间向矢量空间的直接拓展。首先分析了网格编码矢量量化的原理，在第三章矢量量化的基础上详细介绍了小波图像分类加权TCVQ算法的原理和实现过程，并给出了TCVQ在小波图像量化中的应用实例和仿真结果。最后对本章算法作出总结分析。
第六章在TCVQ基础上运用小码书扩展和块编码的思想方法提出在二维码
电子科技大学搏士论文
书空间进行量化的方案，即二维 TCVQ。设计了二维 TCVQ应用于图像编码量化
的算法，给出了二维TCVQ在图像空域的仿真结果和分析。
Based on the analysis of image wavelet transformation and the space/frequency distributing characteristics of different subbands' coefficients, this dissertation fully exploits the following theories and methods: scalar quantization, vector quantization, trellis coded quantization, trellis coded vector quantization, vector classification, codebook expansion and weighted mean square error rule basing mankind visual characteristics,etc. From different angles of information amalgamation, It develops several innovative algorithms of image compression and coding, gives their realization schemes, and makes plentiful simulation tests. The results show their efficiency and validity.
Chapter 2 first reviews the basic theory concerned with image wavelet transformation, which includes the wavelet multi-resolution analysis theory, the discrete wavelet transformation and the two dimension discrete wavelet transformation (Mallat algorithm), and analyzes the space and frequency distributing characteristics of image wavelet coefficients. Then it proposes a new fast zerotree encoded algorithm combing wavelet image zerotree data-structure and optimum quantization, designs a realization scheme and gives simulation results and their analysis.
Chapter 3 particularly introduces vector quantization and its applications to wavelet image quantization. It gives three effective coding schemes, firstly it reviews the fundamental theory of vector quantization and its current algorithms, then it analyzes and summarizes characteristics of wavelet image quantization with VQ. Three commixed still image coding algorithms are proposed based on them and such ideas as: zerotree coding, WMSE (which is based on mankind visual characteristics), classified vector quantization with different vector
structures and classification methods. The chapter gives the principium analysis, realization schemes and simulation results of the proposed algorithms. At last, conclusions are drawn on these algorithms.
Based on scalar quantization of chapter 2,Chapter 4 introduces trellis coded quantization to improve quantization gain. Since the fundamental idea of TCQ originates from trellis coded modulation, in order to comprehend TCQ, it firstly introduces TCM. Also, because Viterbi decoding algorithm is the key of coding gain of TCM and TCQ, it particularly introduces convolutional coding and Viterbi decoding algorithm. Based on them it emphatically describes TCQ principium and analyzes its performance. Following on the heels of these it proposes wavelet image TCQ algorithm, and analyzes its principium and its TCQ algorithm realization. Finally, it gives simulation results and its analysis.
Chapter 5 is the direct extension of chapter 2, namely it maps SQ field to VQ field. This chapter first introduces the principium of trellis coded vector quantization, then focuses on analyzing the algorithm of wavelet image classified weighted TCVQ and its realization process, also gives TCVQ application to wavelet image coding and its simulation results. Finally, it draws conclusions to the algorithms of this chapter.
Using TCVQ and ideas of small codebook expansion and block coding, Chapter 6 proposes a new quantization precept making quantization on two dimension codebook space, that is 2DJTCVQ. It designs an algorithm applying 2DJTCVQ to image quantization, also gives analysis and simulation results of 2D-TCVQ in image space domain.
引文
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