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。     

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.     

一种简单、快速的码书训练算法

以LBG算法为代表的传统的码书训练算法基本上都用量化失真序列收敛作算法停止条件.本文提出了一种简单、快速的新算法.它的基本思想是,不必计算量化失真,直接用区域序列中各区域的元素个数所成序列收敛作停止条件.该算法与经典的LBG算法相比,结构更简单、速度更快、更容易理解和控制.我们用典型的测试图像Lena、Barbara做实验.实验结果表明,该算法与著名的LBG算法的PSNR相差小于0.1dB,但它的运行速度比LBG快2倍以上.     

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.     

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。     

一种新的语音LSF矢量量化码书优化算法

LSF(线谱频率)码书的性能对合成语音质量有着重要影响.经典的LBG算法容易陷入局部最优,而目前的一些码书进化算法搜索空间较大、搜索效率不明显.本文提出了一种新型的基于对LSF矢量空间进行拉伸变化的混合进化码书优化算法.该算法编码空间与矢量同维,相对较小,便于优化操作.算法中引入EP中的变异操作对PSO位置、速度矢量进行控制,以提高优化搜索算法的效率.实验结果表明,本文算法有效地改善了码书性能.     

鲁棒视觉词汇本的自适应构造与自然场景分类应用

该文提出了一种视觉词汇本的优化构造策略。首先引入条件数定量评估海量低层特征的稳定性,排除病态特征,筛选稳定的鲁棒视觉特征;通过分析聚类和降维的内在联系,构造了具有聚类结构的视觉特征自适应降维算法;进而利用低维聚类结构信息中的邻域支持度,自适应选取最佳的初始视觉词汇,同时选择Sil指标作为目标函数,从而改进流行的LBG词汇本生成算法敏感于初始点的随机选取,并只能得到局部最优等不足。新的视觉词汇本生成算法具有聚类和降维的统一计算功能、良好的鲁棒性和自适应优化等特性。基于概率潜在语义分析技术将该文的视觉词汇本应用于自然场景分类,在13类场景图像库上取得了73.46%的平均分类率。     

分区域收敛的快速码书训练算法

该文提出分区域收敛的快速码书训练算法LC,它与LBC算法相比,结构简单、速度快,用典型的测试图像Lena和Barbara做实验,表明LC算法峰值信噪比只比LBG算法少2％左右,但运行速度东LBG的4.61-13.6倍。在比特率为0.375 bpp条件下,LC算法与LBG算法的重建图像质量无明显差别。     

Efficient coding of homogeneous textures using stochastic vectorquantisation and linear prediction

Vector quantisation (VQ) has been extensively used as an effective image coding technique. One of the most important steps in the whole process is the design of the codebook. The codebook is generally designed using the LBG algorithm which uses a large training set of empirical data that is statistically representative of the images to be encoded. The LBG algorithm, although quite effective for practical applications, is computationally very expensive and the resulting codebook has to be recalculated each time the type of image to be encoded changes. Stochastic vector quantisation (SVQ) provides an alternative way for the generation of the codebook. In SVQ, a model for the image is computed first, and then the codewords are generated according to this model and not according to some specific training sequence. The SVQ approach presents good coding performance for moderate compression ratios and different type of images. On the other hand, in the context of synthetic and natural hybrid coding (SNHC), there is always need for techniques which may provide very high compression and high quality for homogeneous textures. A new stochastic vector quantisation approach using linear prediction which is able to provide very high compression ratios with graceful degradation for homogeneous textures is presented. Owing to the specific construction of the method, there is no block effect in the synthetised image. Results, implementation details, generation of the bit stream and comparisons with the verification model of MPEG-4 are presented which prove the validity of the approach. The technique has been proposed as a still image coding technique in the SNHC standardisation group of MPEG     

混合粒子对优化算法在说话人识别中的应用

在粒子群优化(Particle Swarm Optimization, PSO)和混合蛙跳算法(Shuffled Frog-Leaping Algorithm, SFLA)的基础上,该文提出了一种新的混合粒子对优化(Shuffled Particle-Pair Optimizer, SPPO)算法,应用于矢量量化的说话人识别。该算法将全局信息交换和局部深度搜索相结合寻求最佳的说话人码本。群体按适应值分为3个粒子对,每个粒子对由两个粒子构成,按先后顺序执行PSO算法中的速度位置更新和LBG算法以实现局部细致搜索,间隔一定的迭代次数通过SFLA混合策略实现粒子对间的信息交换,从而使群体向全局最优解靠近。实验结果表明,本算法始终稳定地取得显著优于LBG,FCM,FRLVQ-FVQ和PSO算法的说话人识别性能,较好地解决了初始码本影响的识别性能的问题,且在计算时间和收敛速度方面有相当的优势。     

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     

基于遗传算法的矢量量化

本文提出了一种基于遗传算法的矢量化方法。矢量量化码书设计本质是搜索训练矢量的最佳分类。遗传算法有卓越的全局优化搜索能力,易搜索到全局最优的矢量分类,形成高度优化的码书,可克服传统方法局部优化的缺陷。该算法不依赖初始条件、鲁棒性好、结构规则、并行性高。     

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.     

基于混合遗传算法的码书设计方法

将遗传算法与LBG算法相结合,得到了一种矢量量化码书设计算法.利用遗传算法的全局优化能力得到最优的矢量量化码书;同时,克服了传统遗传算法收敛速度慢的缺点.实验结果表明,文中提出的算法性能上优于LBG算法,且收敛速度较快.     

On the computational complexity of the LBG and PNN algorithms

This correspondence compares the computational complexity of the pair-wise nearest neighbor (PNN) and Linde-Buzo-Gray (LBG) algorithms by deriving analytical expressions for their computational times. It is shown that for a practical codebook size and training vector sequence, the LBG algorithm is indeed more computationally efficient than the PNN algorithm.     

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.     

一种不需要计算量化失真的快速码书训练算法

该文利用LBG算法迭代过程中质心序列收敛特性,提出了一种快速算法。它的基本思想是,直接去掉LBG算法中量化失真计算,用质心序列收敛作停止条件。我们用典型的测试图像Lena做实验,实验结果表明,该算法与著名的LBG算法的PSNR相差小于0.1dB,但它的运行时间至少比LBG的运行时间少一半。     

一种改进的基于Hadamard域的码书设计算法

基于Hadamard变换和K-means理论,针对Chen的初始码书设计算法的随机性较强和峰值信噪比(PSNR)不高这两个缺点,提出了一种改进的码书设计算法。本算法利用统计特征量的分类平均法生成初始码书,然后提高求质心的频率,每当一个训练矢量被分类到胞腔时,就求出相应胞腔的质心来代替原有的码字。该算法结合LBG算法的优点,调整后的码字代表了整个胞腔的特性,加速了码书的收敛速度,提升了码书的性能。仿真实验结果表明,较Chen的算法图像效果,即峰值信噪比(PSNR),平均提高了0.5 dB,在迭代次数较小时甚至达0.9 dB。