一种快速模糊矢量量化图像编码算法

本文在学习矢量量化和模糊矢量量化算法的基础上,设计了一种新的训练矢量超球体收缩方案和码书学习公式,提出了一种快速模糊矢量量化算法。该算法具有对初始码书选取信赖性小,不会陷入局部最小和运算最小的优点。实验表明,ＦＦＶＱ设计的图像码书性能与ＦＶＡ算法相比,训练时间大大缩短,峰值信噪比也有改善。     

基于联合准则的码书设计算法

提出了一种矢量量化中码书设计的优化算法。该算法将最大梯度准则与概率匹配准则相结合,在生成初始码书时使速率与失真的调整达到最优。计算机仿真结果表明,该算法减少了码书设计时间,提高了码书性能。     

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.     

图像矢量量化——改进的自组织特征映射神经网络的算法

图像矢量量化（VQ）是图像压缩算法中的重要环节,在VQ中起决定性因素的是构造出性能优异的码书。为改善矢量量化码书的性能,文中在分析Kohonen自组织特征映射（SOFM）的基础上,提出一种识别距离SOFM的算法,同时将矢量量化应用于图像的小波变换域。测试结果表明,改进的算法使码书设计的计算量得到明显的降低,而且码书的性能得到了提高。     

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.     

一种随机竞争学习矢量量化图像编码算法

本文分析了确定性模拟退火技术、竞争学习算法在图像编码中的压缩机理,提出了一种新的随机竞争学习矢量量化算法.该算法将竞争过程与代价函数最小化结合起来,在学习过程中引入模拟退火,并针对矢量量化图像编码的特点,提出了新的参数选取策略,具有对初始码书依赖性小,不会局部最小,收敛速度快,码书性能好等优点.文中还通过计算机实践对该方法进行了性能分析,验证了算法的有效性和鲁棒性.     

基于快速胞腔划分的改进分裂法矢量量化码书设计

本文提出了基于快速胞腔划分的改进分裂法语音信号矢量量化码书设计新算法。新算法应用特征变量对码书进行合理的构造，使得码书在结构上具有秩序性。利用这种秩序性进行快速划分胞腔，从而压缩分裂法码书训练中庞大的运算量。计算机实验结果表明，新算法明显减少了码本训练的复杂度，提高了矢量量化码书的训练速度。实验还分析了码书扩展参数r的不同取值对码书性能的影响．     

一种用于图象编码的神经网络及其改进算法

本文用Kohonen的自组织特征映射神经网络设计图象矢量量化的码书,研究了网络的基本性质和学习算法的实现,提出了对学习算法的改进方法。实验结果表明,自组织特征映射神经网络能够有效地用于构造图象矢量量化的码书,算法简洁,实现快速,编码效果优良。     

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.     

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。     

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.     

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     

Fine-coarse vector quantization

A fast method for searching an unstructured vector quantization (VQ) codebook is introduced and analyzed. The method, fine-coarse vector quantization (FCVQ), operates in two stages: a `fine' structured VQ followed by a table lookup `coarse' unstructured VQ. Its rate, distortion, arithmetic complexity, and storage are investigated using analytical and experimental means. Optimality condition and an optimizing algorithm are presented. The results of experiments with both uniform scalar quantization and tree-structured VQ (TSVQ) as the first stage are reported. Comparisons are made with other fast approaches to vector quantization, especially TSVQ. It is found that when rate, distortion, arithmetic complexity, and storage are all taken into account, FCVQ outperforms TSVQ in a number of cases. In comparison to full search quantization, FCVQ has much lower arithmetic complexity, at the expense of a slight increase in distortion and a substantial increase in storage. The increase in mean-squared error (over full search) decays as a negative power of the available storage     

基于人工蚁群优化的矢量量化码书设计算法

本文提出一种基于人工蚁群优化的矢量量化码书设计新算法.该算法利用人工蚁群系统中蚂蚁通过信息素留存寻找最优路径的机制,结合单只蚂蚁通过拾起、放下物体从而使物体聚堆的行为模式,合理设计放下概率、禁忌列表、信息素更新方式以及相应的参数.与基于进化模拟退火和随机竞争学习的码书设计算法相比,本文提出的算法能获得性能较好的码书,其峰值信噪比比传统的LBG算法提高超过2dB.     

ENHANCED MULTISTAGE VECTOR QUANTIZATION FOR SAR RAW DATA COMPRESSION

Multistage Vector Quantization(MSVQ) can achieve very low encoding and storage complexity in comparison to unstructured vector quantization. However, the conventional MSVQ is suboptimal with respect to the overall performance measure. This paper proposes a new technology to design the decoder codebook, which is different from the encoder codebook to optimise the overall performance. The performance improvement is achieved with no effect on encoding complexity, both storage and time consuming, but a modest increase in storage complexity of decoder.     

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。     

Fast Image Coding Algorithm Using Indirect-Index Codebook Based on SMVQ

Side-match vector quantization (SMVQ) achieves better compression performance than vector quantization (VQ) in image coding due to its exploration of the dependence of adjacent pixels. However, SMVQ has the disadvantage of requiring excessive time during the process of coding. Therefore, this paper proposes a fast image coding algorithm using indirect-index codebook based on SMVQ (ⅡC-SMVQ) to reduce the coding time. Two codebooks, named indirect-index codebook (Ⅱ-codebook) and entire-state codebook (ES-codebook), are trained and utilized. The Ⅱ-codebook is trained by using the Linde-Buzo-Gray (LBG) algorithm from side-match information, while the ES-codebook is generated from the clustered residual blocks on the basis of the Ⅱ-codebook. According to the relationship between these two codebooks, the codeword in the Ⅱ-codebook can be regarded as an indicator to construct a fast search path, which guides in quickly determining the state codebook from the ES-codebook to encode the to-be-encoded block. The experimental results confirm that the coding time of the proposed scheme is shorter than that of the previous SMVQ.     

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。     

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.