人工蚁群聚类码书设计算法

文中将频率敏感算法引入到基本的蚁群算法中,提出了一种改进的蚁群聚类码书设计算法。在提出的码书设计算法中采用LBG码书优化准则,引入了频率敏感算法。仿真实验表明,提出的算法避免了停滞现象发生,有效地提高了其全局搜索能力。     

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.     

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.     

改进模拟退火算法在矢量量化编码中的应用

为了提高模拟退火算法的最终解的质量,文中对控制算法进程的冷却进度表进行了优化选取,尤其在控制马尔可夫链长方面,给出了依据算法搜索过程的反馈信息来控制马尔可夫链长的方法.将该算法与LBG算法相结合,应用于矢量量化图像编码,既保持了模拟退火对初始码书依赖性小、不容易陷入局部极值的优点,又具备LBG算法的易于实现和计算量小的特点.仿真实验表明,该算法提高了码书的编码性能.     

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。     

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.     

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.     

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。     

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.     

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.     

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.     

一种基于禁忌搜索的多用户检测方法

本文提出一种实现码分多址(ＣＤＭＡ)系统上多用户检测(ＭＵＤ)的禁忌搜索(ｔａｂｕｓｅａｒｃｈ)的方法。 该方法利用传统检测方法的输出作为初始解,直接应用禁忌搜索算法来解决最佳多用户检测的非线性优化组合问题。通过分析以及对同步和异步情况的仿真表明,该方法简单易于实现,具有多项式的计算复杂度,对远近问题不敏感,并且能够得到与最佳检测方法(ＯＤ)非常接近的误码率性能和抗多址干扰性能．     

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     

Comparing Three Heuristic Search Methods for Functional Partitioning in Hardware–Software Codesign

This paper compares three heuristic search algorithms: genetic algorithm (GA), simulated annealing (SA) and tabu search (TS), for hardware–software partitioning. The algorithms operate on functional blocks for designs represented as directed acyclic graphs, with the objective of minimising processing time under various hardware area constraints. Thecomparison involves a model for calculating processing time based on a non-increasing first-fit algorithm to schedule tasks, given that shared resource conflicts do not occur. The results show that TS is superior to SA and GA in terms of both search time and quality of solutions. In addition, we have implemented an intensification strategy in TS called penalty reward, which can further improve the quality of results.     

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。