基于微粒群的矢量量化码书设计算法

针对在LBG算法中存在初始码书的选择极易影响码书训练的收敛速度和最终码书性能的缺陷,提出了一种基于微粒群的矢量量化码书设计算法.首先产生具有一定全局性特点的初始码书,然后再应用LBG算法进行优化得到同时具有局部特性的码书.实验结果验证了该算法的合理性.     

高效的模糊聚类初始码书生成算法

码书设计在矢量量化中至关重要,而多数码书设计算法都是基于初始码书的.从经典的LBG算法的缺陷出发,提出一种基于模糊聚类的高效初始码书生成算法,通过将初始码书的码矢在输入矢量空间中很好地散开,并尽可能占据输入概率密度较大的区域,从而使之后的LBG算法避免陷入局部最优,设计出的码书性能更好,更加接近全局最优,同时加快了收敛速度,减少了迭代次数.将该算法应用于图像编码的实验中,结果表明:该算法能够从效率和质量两方面有效地提高矢量量化的性能.     

基于量子粒子群优化算法的图像矢量量化码书设计

鉴于经典的LBG码书设计算法易陷入局部最优解,将量子粒子群优化算法应用到图像矢量量化码书设计中,提出一种基于量子粒子群的矢量量化码书设计算法(QPSO-VQ)。在该算法中,用粒子表示码书,用峰值信噪比(PSNR)作为算法的适应度函数,通过量子粒子群算法的更新公式来更新码书。实验结果表明,与经典的LBG码书设计算法和粒子群矢量量化码书设计算法相比,QPSO-VQ在解码图像的PSNR值和算法的稳定度等方面有比较明显的优势,可以获得性能较好的码书。     

语音LSF参数M-L搜索VQ算法研究

针对高效低速率语音编码,以LBG矢量量化码书设计算法为基础,研究了M-L搜索多级矢量量化(VQ)的码书设计算法和M-L搜索多级矢量量化编解码算法,同时对整个算法进行了全面的测试和性能分析。设计结果表明:该方法可有效提高LSF参数压缩的效率,改善谱失真指标。     

等误差原则在进化算法优化矢量量化中的应用

文中利用进化算法优化矢量量化器设计,在选择后代码书矢量时,利用等误差原则选择获胜后代码书矢量.算法采用LBG算法作为基本聚类算法,利用所选后代码矢调整相应区域的父代码矢,减小各区域子误差,改善总的期望误差.试验证明了此方法的有效性,解决了LBG算法局部最优的局限,获取更接近全局最优的码书.     

粒子对算法在图像矢量量化中的应用

本文给出了一种新的图像矢量量化码书的优化设计方法——粒子对算法.在传统粒子群优化(Particle Swarm Optimization,PSO)算法的基础上,用两个粒子构成了群体规模较小的粒子对,在码书空间中搜索最佳码书.在每次迭代运算中,粒子对按先后顺序执行PSO算法中的速度更新、位置更新操作和标准LBG算法,并用误差较大的训练矢量代替越界的码字.此算法避免粒子陷入局部最优码书,较准确地记录和估计每个码字的最佳移动方向和历史路径,在训练矢量密集区域和稀疏区域合理地分配码字,从而使整体码书向全局最优解靠近.实验结果表明,本算法始终稳定地取得显著优于FKM、FRLVQ、FRLVQ-FVQ算法的性能,较好地解决了矢量量化中初始码书影响优化结果的问题,且在计算时间和收敛速度方面有相当的优势.     

免疫粒子群改进LBG的孤立词语音识别算法研究

语音识别技术已在通信及控制等领域得到广泛应用,针对孤立词语音识别矢量量化中LBG算法对初始码书选择敏感,容易陷入局部最优、泛化能力不强的缺点,将免疫粒子群优化算法(IPSO)和LBG算法结合进行聚类分析,从而得到基于IPSO-LBG的码书设计方法,并将其用于基于离散隐马尔可夫模型(DHMM)的孤立词语音识别系统中。通过实验,与传统LBG算法的DHMM孤立词语音识别系统的识别结果相比,证明了改进的系统有较好的识别率和适应性。     

基于改进禁止搜索算法的矢量量化码书设计

本文提出了基于改进禁止搜索(TS)算法的矢量量化(VQ)码书设计方法.禁止搜索算法的关键是如何定义一个解以及如何在当前解的基础上生成邻域解.由于码书设计的两个优化准则是最邻近条件和聚类质心条件,本文提出了两种禁止搜索算法的解描述方案,其相应算法分别叫基于码书的禁止搜索(CB-TS)算法和基于聚类划分的禁止搜索(PB-TS)算法.为了提高禁止搜索算法的性能,文中在禁止搜索算法中融入了模拟退火(SA)机制.为了进一步提高码书性能,文中还将码书设计的传统LBG算法融入禁止搜索算法中.结果表明,基于禁止搜索的两种码书设计方案所生成的码书性能都比LBG算法有明显提高.     

一种基于混合蚁群算法的矢量量化码书设计算法

提出了一种基于混合蚁群算法的矢量量化码书设计算法。该算法首先通过自适应地调整截取转移概率的参数,加大蚁群算法的搜索最优解的力度;然后以蚁群算法搜索的结果作为初始解,利用改进的LBG算法作进一步的搜索,从而加快算法的收敛速度。实验结果表明,该算法不但大大提高码书性能,而且也缩短了运行时间,解码恢复图像能获得较高的主、客观质量。     

采用遗传算法的VQ码本设计及说话人识别

矢量量化(VQ)方法是文本无关说话人识别中广泛应用的建模方法之一。在矢量量化过程中,经典的LBG算法收敛速度快,但极易收敛于局部最优点,无法保证根据有限样本数据得到最优码本,并最终影响系统识别性能。考虑到遗传算法(GA)是一种具有全局化寻优搜索能力的算法,本文提出了遗传算法和K均值算法相结合的综合分析方法GA-K进行码本设计,改善了码本的质量。讨论了具体的算法实现,分析了在不同的特征参数LPCC及MFCC、不同测试语音长度下的说话人识别性能。实验结果显示,GA-K方法优于传统的LBG算法,可以很好地协调收敛性和识别率之间的关系。     

A comparison of several vector quantization codebook generationapproaches

A review and a performance comparison of several often-used vector quantization (VQ) codebook generation algorithms are presented. The codebook generation algorithms discussed include the Linde-Buzo-Gray (LBG) binary-splitting algorithm, the pairwise nearest-neighbor algorithm, the simulated annealing algorithm, and the fuzzy c-means clustering analysis algorithm. A new directed-search binary-splitting method which reduces the complexity of the LBG algorithm, is presented. Also, a new initial codebook selection method which can obtain a good initial codebook is presented. By using this initial codebook selection algorithm, the overall LBG codebook generation time can be reduced by a factor of 1.5-2.     

基于PSO-LBG算法的最优码本设计

矢量量化（VQ）是一种极其重要的信号压缩方法，广泛地应用于图像信号压缩、语音信号压缩领域。它I的主要问题是码本设计，在码本设计过程中，有许多算法被提出。本文提出了PSO和LBG算法相结合的1PSO—LBG算法采设计码本，改善了码本质量，提高了收敛速度。     

说话人辨认系统中码本生成的新算法

提出了一种新的适用于离散HMM说话人辨认系统的VQ码本训练方法,码本的训练准则是使码本中各码字的利用率趋于均等.将新方法训练的码本与用LBG算法训练的码本进行了比较,实验表明,在基于离散HMM的说话人辨认系统中,用新方法训练的码本性能优于用LBG算法训练的码本,特别是在与文本无关的情况下,使系统的正确辨认率显著提高.     

Feature-based initialisation for image vector quantisation codebookgeneration

A feature-based initialisation method is developed for codebook design in vector quantisation of images. The proposed algorithm makes use of different features of the image block in training set-to-do clustering for producing the initial codebook for the LBG algorithm. It is shown that the proposed method not only speeds up the LBG process but also enhances the quality of the reproduced images     

A fast finite-state algorithm for vector quantizer design

The Linde-Buzo-Gray (LBG) algorithm is usually used to design a codebook for encoding images in vector quantization. In each iteration of this algorithm, one must search the full codebook in order to assign the training vectors to their corresponding codewords. Therefore, the LBG algorithm needs a large computation effort to obtain a good codebook from the training set. The authors propose a finite-state LBG (FSLBG) algorithm for reducing the computation time. Instead of searching the entire codebook, they search only those codewords that are close to the codeword for a training vector in the previous iteration. In general, the number of these possible codewords can be made very small without sacrificing performance. By only searching a small part of the codebook, the computation time is reduced. In experiments, the performance of the FSLBG algorithm in terms of signal-to-noise ratio is very close to that of the LBG algorithm. However, the computation time of the FSLBG algorithm is about 10% of the time required by the LBG algorithm     

Codebook Design for Vector Quantization Based on?a?Kernel Fuzzy Learning Algorithm

Vector quantization (VQ) is an efficient technique for data compression and has been successfully used in various applications. The methods most commonly used to generate a codebook are the Linde, Buzo, Gray (LBG) algorithm, fuzzy vector quantization (FVQ) algorithm, Kekre‘s Fast Codebook Generation (KFCG) algorithm, discrete cosine transform based (DCT-based) codebook generation method, and k-principle component analysis (K-PCA) algorithm. However, if the separation boundaries in codebook generation are nonlinear, their performance can degrade fast. In this paper, we present a kernel fuzzy learning (KFL) algorithm, which takes advantages of the distance kernel trick and the gradient-based fuzzy clustering method, to create a codebook automatically. Experiments with real data show that the proposed algorithm is more efficient in its performance compared to that of the LBG, FVQ, KFCG, and DCT-based method, and to the K-PCA algorithm.     

一种新的基于遗传算法的VQ码本优化方法

矢量量化(VQ)是语音识别中广泛应用的一种数据压缩和编码方法。在矢量量化的码本设计过程中,经典的LBG算法收敛速度快,但极易陷入局部最优,且初始码本的生成对最佳码本的设计影响很大。考虑到遗传算法(GA)是一种具有全局优化搜索能力的算法,提出了GA和LBG算法相结合的GA L算法来优化码本,改善了码本质量,并将其应用于非特定人汉语连续数字语音识别中。实验结果表明,GA L算法优于传统的LBG算法。     

Performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches in VQ codebook generation for image compression

The aim of this study is to generate vector quantisation (VQ) codebooks by integrating principle component analysis (PCA) algorithm, Linde-Buzo-Gray (LBG) algorithm, and evolutionary algorithms (EAs). The EAs include genetic algorithm (GA), particle swarm optimisation (PSO), honey bee mating optimisation (HBMO), and firefly algorithm (FF). The study is to provide performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches. The PCA-EA-LBG approaches contain PCA-GA-LBG, PCA-PSO-LBG, PCA-HBMO-LBG, and PCA-FF-LBG, while the PCA-LBG-EA approaches contain PCA-LBG, PCA-LBG-GA, PCA-LBG-PSO, PCA-LBG-HBMO, and PCA-LBG-FF. All training vectors of test images are grouped according to PCA. The PCA-EA-LBG used the vectors grouped by PCA as initial individuals, and the best solution gained by the EAs was given for LBG to discover a codebook. The PCA-LBG approach is to use the PCA to select vectors as initial individuals for LBG to find a codebook. The PCA-LBG-EA used the final result of PCA-LBG as an initial individual for EAs to find a codebook. The search schemes in PCA-EA-LBG first used global search and then applied local search skill, while in PCA-LBG-EA first used local search and then employed global search skill. The results verify that the PCA-EA-LBG indeed gain superior results compared to the PCA-LBG-EA, because the PCA-EA-LBG explores a global area to find a solution, and then exploits a better one from the local area of the solution. Furthermore the proposed PCA-EA-LBG approaches in designing VQ codebooks outperform existing approaches shown in the literature.     

Multiplication free vector quantization usingL1 distortion measure and its variants

The author considers vector quantization that uses the L (1) distortion measure for its implementation. A gradient-based approach for codebook design that does not require any multiplications or median computation is proposed. Convergence of this method is proved rigorously under very mild conditions. Simulation examples comparing the performance of this technique with the LBG algorithm show that the gradient-based method, in spite of its simplicity, produces codebooks with average distortions that are comparable to the LBG algorithm. The codebook design algorithm is then extended to a distortion measure that has piecewise-linear characteristics. Once again, by appropriate selection of the parameters of the distortion measure, the encoding as well as the codebook design can be implemented with zero multiplications. The author applies the techniques in predictive vector quantization of images and demonstrates the viability of multiplication-free predictive vector quantization of image data.