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量化方法。矢量量经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。该方法不依赖初始条件、鲁棒性好、结构规则、并行性高。     

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.     

基于粒子群算法的码书设计研究

由于应用基本粒子群进行码书设计时容易出现陷入局部最优解的问题,故引进模拟退火算法对全局极值的更新条件做了改进,提出了一种新的码书设计方法.改进算法的全局极值更新条件采用了随机概率扰动接受的方式,既接收优化解,也可以接受恶化解,从而增加全局最优区域的搜索能力,避免了粒子过早的"趋同性".采用提出的码书设计方法进行于语音矢量量化表明新提出的算法所重构的语音无论是从清晰度、自然度还是理解性上都要好于基本粒子群算法所重构的语音.增大全局搜索范围.     

代数码书分级分段优化搜索方法研究

代数码书作为一种流行的固定码书结构,其搜索方法直接影响解码语音质量及计算复杂度.本文提出了一种代数码书分级分段优化搜索GSOS(Graded and Subsection Optimization Search)方法.GSOS方法融合了脉冲替代法、分段搜索及分级优化方法的优点,通过创建优质的初始码书,达到提高初始码书质量的目的,并将码书矢量的乘法运算用分段子码书的加法运算替代,同时将脉冲按贡献不同进行分级,由此提高脉冲替代优化的搜索效率;将该方法用于自适应多速率宽带语音编码器AMR-WB固定码书搜索阶段,实验结果表明,所提出的搜索方法可使固定码书搜索计算量降为深度优先树搜索方法的13.75％,但解码语音质量只降低了4.01％,主观听觉感受基本感觉不出差异.     

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.     

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.     

基于遗传算法的不等误差保护方法

提出一种对嵌入式编码提供不等误差保护方法,采用遗传算法全局优化的迭代进化选择最优的信道码,将等误差保护的信道码作为初始条件,不断进化搜索最优的信道码集。相对于动态规划、局部搜索等算法,遗传算法更适用于不同准则下的不等误差保护方案。仿真结果表明这种算法计算简单,且可选用等误差保护作为初始条件,减少搜索时间。     

基于Memetic算法的DNA序列数据压缩方法

该文提出一种基于CPMA(Collaborative Particle swarm optimization-based Memetic Algorithm) 算法的DNA序列数据压缩方法,CPMA分别采用综合学习粒子群优化(Comprehensive Learning Particle Swarm Optimization, CLPSO)算法和动态调整的混沌搜索算子(Dynamic Adjustive Chaotic Search Operator, DACSO)进行全局搜索和局部搜索。该文采用CPMA寻找全局最优的基于扩展操作的近似重复矢量(Extended Approximate Repeat Vector, EARV)码书,并用此码书压缩DNA序列数据。实验结果表明,CPMA比其它优化算法有很大的改善,对文中采用的大部分测试函数,其解都非常接近全局最优点;对于DNA基准测序序列,与文中所列的经典DNA序列压缩算法相比,基于CPMA算法的压缩性能得到了显著提升。     

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.     

一种高效的基于矢量量化的彩色视频图像压缩编码方法

针对彩色视频图像提出了一种DCT域内基于矢量量化的高效编码方法。为去掉彩色图像各分量间的相关性,首先将图像由RGB空间转换到YUV空间,然后根据人类视觉特征（HVS）对色度信号U、V进行了亚采样和平均化处理;对亮度信号Y则进行分块DCT变换,并根据HVS特征对变化域内的块矢量进行自适应分类,然后根据矢量的类型分别构造码矢和进行全局码书设计。提出的全局码书设计方案可以根据帧间相关性及码字使用频率,对码书的内容自动进行更新和替换,以适应场景内容的变化。实验结果表明：在保证图像重建质量的前提下,本文提出的方法具有较高的压缩效率,比较适合于视频会议以及水下视频观测等应用场合。     

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。     

模糊强化学习型的图像矢量量化算法

本文给出了一种新的图像矢量量化码书的优化设计方法.传统矢量量化方法只考虑了码字与训练矢量之间的吸引影响,所以约束了最优解的寻解空间.本文提出了一种新的学习机理--模糊强化学习机制,该机制在传统的吸引因子基础上,引入新的排斥因子,极大地释放了吸引因子对最优解的寻解空间的约束.新的模糊强化学习机制没有采用引入随机扰动的方法来避免陷入局部最优码书,而是通过吸引因子和排斥因子的合力作用,较准确地确定了每个码字的最佳移动方向,从而使整体码书向全局最优解靠近.实验结果表明,基于模糊强化学习机制的矢量量化算法始终稳定地取得显著优于模糊K-means算法的性能,较好地解决了矢量量化中的码书设计容易陷入局部极小和初始码书影响优化结果的问题.     

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.     

基于小波理论的矢量量化遥感图像压缩方法

分析了遥感图像的统计特性，提出了适合遥感图像压缩的矢量量化与小波变换相结合的压缩方法。该方法将遥感图像小波变换后高频子图划分为一定大小的的像块，对局部相关性较强、灰度变化较小的像块进行高倍压缩；对局部相关性较小、灰度变化较大的像块进行高保真压缩。实验表明，本方法具有良好的压缩性能，适用于遥感图像的压缩。     

Training ratio and comparison of trained vector quantizers

The vector quantizer (VQ) codebook is usually designed by clustering a training sequence (TS) drawn from the underlying distribution function. In order to cluster a TS, we may use the K-means algorithm (generalized Lloyd (1982) algorithm) or the self-organizing map algorithm. In this paper, a survey of trained VQ performance is conducted to study the effect of the training ratio on training quantizers. The training ratio, which is defined by the ratio of the TS size to the codebook size, is dependent on the VQ structure. Hence, different VQs may show different training properties, even though the VQs are designed for the same TS. A numerical comparison of trained VQs is then conducted in conjunction with deriving their training ratios. Through the comparison, it is shown that structured VQs can achieve better performance than the full-search scheme if the codebooks are trained by a finite TS. Further, we can derive a design or comparison guideline that maintains equal training ratios in training different VQs.     

Joint optimization of VQ codebooks and QAM signal constellationsfor AWGN channels

A joint design scheme has been proposed to optimize the source encoder and the modulation signal constellation based on the minimization of the end-to-end distortion including both the quantization error and channel distortion. The proposed scheme first optimizes the vector quantization (VQ) codebook for a fixed modulation signal set, and then the modulation signals for the fixed VQ codebook. These two steps are iteratively repeated until they reach a local optimum solution. It has been shown that the performance of the proposed system can be enhanced by employing a new efficient mapping scheme between codevectors and modulation signals. Simulation results show that a jointly optimized system based on the proposed algorithms outperforms the conventional system based on a conventional quadrature amplitude modulation signal set and the VQ codebook designed for a noiseless channel     

A vector quantizer with minimum visible distortion

A method for designing codebooks for vector quantization (VQ) based on minimum error visibility in a reconstructed picture is described. The method uses objective measurements to define visibility for the picture being coded. The proposed VQ is switched type, i.e., the codebook is divided into subcodebooks, each of which is related to a given subrange of error visibility. Codebook optimization is carried out on the basis of a particular definition of visible distortion of the reconstructed image. Subjective judgment of the test results, carried out at 0.5 b/pel bit rate, indicates that the proposed VQ enables low-distortion images to be reconstructed even when subcodebooks with a small number of codewords are used, thus reducing the codebook search time to about 10% of that required by a fixed VQ (both inside and outside the training set)     

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.     

Progressive vector quantization on a massively parallel SIMDmachine with application to multispectral image data

This correspondence discusses a progressive vector quantization (VQ) compression approach, which decomposes image data into a number of levels using full-search VQ. The final level is losslessly compressed, enabling lossless reconstruction. The computational difficulties are addressed by implementation on a massively parallel SIMD machine. We demonstrate progressive VQ on multispectral imagery obtained from the advanced very high resolution radiometer (AVHRR) and other earth-observation image data, and investigate the tradeoffs in selecting the number of decomposition levels and codebook training method.