模糊聚类与LBG级联的VQ算法

针对LBG算法初始码本随机选取后易出现空胞腔、易陷入局部极小、迭代次数大等缺陷,本文依据模糊聚类理论引入了矢量量化码本设计训练的模糊聚类与LBG级联算法:先用模糊聚类算法训练码本,将训练得到的码本作为传统LBG算法的初始码本,再用传统LBG算法训练.论述了模糊聚类和LBG联合算法的原理与方法;用该算法分剐训练了语音线性...     

基于遗传LBG的图像矢量量化改进算法

矢量量化在图像压缩中有着举足轻重的地位。码书的设计是算法的关键,经典的LBG聚类算法由于对初始码书的选择非常敏感会导致不同的量化效果。把遗传算法和LBG算法相结合,充分利用LBG算法的局部搜索能力和遗传算法的全局寻优能力,能够在大大改善码本质量的同时加快算法的收敛速度。     

基于Hopfield神经网络的图像矢量量化

矢量量化是图像压缩的重要方法。论文提出了基于Hopfield神经网络的图像矢量量化方法,该方法首先构造聚类表格;然后聚类表格按离散Hopfield神经网络串行方式运行;最后根据得到的最终码字集,对图像进行矢量量化。论文最后给出模拟实验和结果比较,结果表明该方法是有效的,生成的码本质量优于传统的LBG算法。     

说话人识别中采用混合免疫算法的VQ码本设计

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,它的主要问题是码本设计问题。语音特征参数是高维数据,样本分布复杂,因此码本设计的难度也很大,传统的LBG算法只能获得局部最优的码本。提出一种VQ码本设计的新方法,将小生境技术与K-均值算法融入到免疫算法训练过程中,形成混合免疫算法,采用针对高维数据聚类的改进变异算子,降低了随机变异的盲目性,增强群体的全局及局部搜索能力,同时通过接种疫苗提高算法的收敛速度。说话人识别实验表明,与传统LBG和基于混合遗传算法的VQ码本设计方法相比,该方法可以得到更优的模型参数,使得系统的识别率进一步提高。     

利用离散Hopfield网络的码本设计方法

论文提出了一种利用Hopfield网络的码本设计方法,分析了LBG算法和离散Hopfield网络的特点,针对该特点构造聚类表格,并按离散Hopfield神经网络串行方式运行,从而得到最终码字集。通过实验表明,在码本大小相同的情况下,峰值信噪比提高了2.742～3.825 dB,生成的码本质量较传统的LBG算法更加有效。     

基于遗传算法的VQ码本设计及语音识别

在矢量量化（VQ）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,并将其应用于汉语连续数字语音识别中,实验结果表明了GA-L算法的有效性。     

说话人识别中随机局部搜索算法的研究

目前在矢量量化的码本训练中经典的聚类方法是LBG算法,但该算法的主要缺陷是对初始码书的依赖性较大,容易过早地陷入局部极小.本文在基于矢量量化的说话人识别中研究了一种随机局部搜索的聚类算法.该算法不依赖初始条件,结构规则,容易实现,效果好,具有很优越的全局优化搜索能力,在语音参数聚类实验中表现出了很好的性能,得到的码书质量也优于经典的LBG-算法,从而为在基于矢量量化的说话人识别中设计准全局最优码书提供了一种新思路.     

采用遗传算法的文本无关说话人识别

为解决在说话人识别方法的矢量量化(Vector Quantization,VQ)系统中,K-均值法的码本设计很容易陷入局部最优,而且初始码本的选取对最佳码本设计影响很大的问题,将遗传算法(Genetic Algorithm,GA)与基于非参数模型的VQ相结合,得到1种VQ码本设计的GA-K算法.该算法利用GA的全局优化能力得到最优的VQ码本,避免LBG算法极易收敛于局部最优点的问题;通过GA自身参数,结合K-均值法收敛速度快的优点,搜索出训练矢量空间中全局最优的码本.实验结果表明,GA-K算法优于LBG算法,可以很好地协调收敛性和识别率之间的关系.     

浊音隶属度参数及F-LBG算法

为了克服低速率声码器因清浊音硬判决、粗判决而导致解码语音有帧过渡等不自然感的缺陷,在分析比较目前主流声码器编码算法中激励参数提取和量化算法的基础上,将模糊数学中的隶属度概念引入语音子带清浊音描述中,提出了5维的浊音隶属度矢量概念,用于精细描述语音丰富的激励信息;介绍了浊音隶属度矢量的提取算法;提出了矢量量化码本的模糊聚类与LBG级联训练算法(F-LBG);用提取算法提取、建立了浊音隶属度码本的训练样本集,用F-LBG训练了浊音隶属度码本;将提取算法和F-LBG法训练得到的浊音隶属度码本分别应用于正弦激励声码器、混合激励声码器和同态声码器进行语音编、解码仿真;结果表明,用浊音隶属度矢量描述和合成语音激励信号的算法,具有较高的准确性和较强的噪声鲁棒性。     

一种增强的LPC参数多级矢量量化技术

为充分利用码本的级间相关性,提出了一种联合码本优化多级矢量量化(JCO-MSVQ)码本设计方法。每次迭代时,先将训练矢量对码字进行聚类,再对各级码本进行联合优化,利用条件期望逐级更新码本。实验数据表明,该算法在设计10维线谱频率(LSF)参数量化码本时,较随机松弛算法(SR)码本有更小的平均量化畸变。23比特/帧LSF参数量化器平均对数谱失真为0.87dB,达到了透明量化要求。     

Image vector quantization algorithm via honey bee mating optimization

The vector quantization (VQ) was a powerful technique in the applications of digital image compression. The traditionally widely used method such as the Linde–Buzo–Gray (LBG) algorithm always generated local optimal codebook. Recently, particle swarm optimization (PSO) is adapted to obtain the near-global optimal codebook of vector quantization. An alternative method, called the quantum particle swarm optimization (QPSO) had been developed to improve the results of original PSO algorithm. In this paper, we applied a new swarm algorithm, honey bee mating optimization, to construct the codebook of vector quantization. The results were compared with the other three methods that are LBG, PSO–LBG and QPSO–LBG algorithms. Experimental results showed that the proposed HBMO–LBG algorithm is more reliable and the reconstructed images get higher quality than those generated from the other three methods.     

一种基于覆盖域密度的LBG算法

针对矢量量化中Linde-Buzo-Gray(LBG)算法产生大量无效或重复码向量问题,提出了一种基于覆盖域密度分割码向量方法.在LBG算法的更新迭代步骤,记录各码向量的覆盖域及其平均失真率,计算覆盖域密度;码向量分割时,只对"坏的"码向量进行分割,"好的"码向量直接复制到下一代码书中.实验表明,修改后的算法提高了LBG的鲁棒性,码书质量也得到一定程度提高.     

Fuzzy declustering-based vector quantization

Vector quantization is a useful approach for multi-dimensional data compression and pattern classification. One of the most popular techniques for vector quantization design is the LBG (Linde, Buzo, Gray) algorithm. To address the problem of producing poor estimate of vector centroids which are subjected to biased data in vector quantization; we propose a fuzzy declustering strategy for the LBG algorithm. The proposed technique calculates appropriate declustering weights to adjust the global data distribution. Using the result of fuzzy declustering-based vector quantization design, we incorporate the notion of fuzzy partition entropy into the distortion measures that can be useful for classification of spectral features. Experimental results obtained from simulated and real data sets demonstrate the effective performance of the proposed approach.     

基于压缩感知理论的分类量化图像编码方法*

针对矢量量化压缩速度慢、图像复原效果不理想等问题,根据图像小波分解后高频子带稀疏的特点,提出了一种基于压缩感知（compressed sensing,CS）理论的分类量化图像编码算法。仿真结果表明,与LBG矢量量化编码算法相比,重构图像质量得到极大提升,在相似压缩比下,该算法取得了较好的效果,PSNR 平均有1～3 dB 的明显提高;在相似信噪比（PSNR）下,该算法在图像压缩方面也有很大改进。     

An evolutionary lion optimization algorithm‐based image compression technique for biomedical applications

Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2‐LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search‐based LBG (CS‐LBG), Firefly‐based LBG (FF‐LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2‐LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS‐LBG, FA‐LBG, and JPEG2000 methods. The experimental values revealed that the L2‐LBG process yielded effective compression performance with a better‐quality reconstructed image.     

基于新的空胞腔策略的矢量量化码书设计方法

为了克服传统LBG算法中的空胞腔现象,提出了一种基于码字间距最大化的新的空胞腔策略。利用离码书距离最大的输入矢量来修改胞腔中的码字,旨在形成码字的合理分布,减小矢量量化的平均失真。实验结果表明：提出的策略能有效地消除空胞腔现象,获得性能较好的码书,其峰值信噪比比传统的LBG算法提高了3 dB。     

Evolutionary fuzzy particle swarm optimization vector quantization learning scheme in image compression

This article develops an evolutional fuzzy particle swarm optimization (FPSO) learning algorithm to self extract the near optimum codebook of vector quantization (VQ) for carrying on image compression. The fuzzy particle swarm optimization vector quantization (FPSOVQ) learning schemes, combined advantages of the adaptive fuzzy inference method (FIM), the simple VQ concept and the efficient particle swarm optimization (PSO), are considered at the same time to automatically create near optimum codebook to achieve the application of image compression. The FIM is known as a soft decision to measure the relational grade for a given sequence. In our research, the FIM is applied to determine the similar grade between the codebook and the original image patterns. In spite of popular usage of Linde–Buzo–Grey (LBG) algorithm, the powerful evolutional PSO learning algorithm is taken to optimize the fuzzy inference system, which is used to extract appropriate codebooks for compressing several input testing grey-level images. The proposed FPSOVQ learning scheme compared with LBG based VQ learning method is presented to demonstrate its great result in several real image compression examples.     

一种采用码字转移的改进型LBG算法

经典LBG算法的局部极小值问题是制约其性能的重要因素．根据渐进最优矢量量化理论的思想提出了一种改进型LBG算法，它采用码字转移的方法使各个类的畸变趋于平衡，从而近一步减小平均畸变以获得性能更优的量化器．文中介绍了若干实验，对多种分布的样本以及2维图像进行了经典算法和改进型算法的比较．从实验结果看出，后者的算法性能大大优于前者．