使用L1失真测度及其派生方法的无乘法矢量量化

提出了一个基于梯度的算法来设计码本。该算法不需要任何乘法或中项计算,其收敛性在非常微弱的条件下得到了严格的证明。仿真举例将该技术与LBG算法的表现进行了比较,结果表明,尽管前者简单,但在平均失真条件下其产生的码本与后者等量。该设计码本的算法进而被扩充为一个具有分段线性特性的失真算法。通过对失真测度的参数的近似选择,编码和码本的设计又一次能在没有乘法的情况下得以实现。将本技术应用于图像的预测矢量量化,并证明了对图像数据进行无乘法预测矢量量化技术的实用价值。     

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.     

A complexity reduction technique for image vector quantization

A technique for reducing the complexity of spatial-domain image vector quantization (VQ) is proposed. The conventional spatial domain distortion measure is replaced by a transform domain subspace distortion measure. Due to the energy compaction properties of image transforms, the dimensionality of the subspace distortion measure can be reduced drastically without significantly affecting the performance of the new quantizer. A modified LBG algorithm incorporating the new distortion measure is proposed. Unlike conventional transform domain VQ, the codevector dimension is not reduced and a better image quality is guaranteed. The performance and design considerations of a real-time image encoder using the techniques are investigated. Compared with spatial domain a speed up in both codebook design time and search time is obtained for mean residual VQ, and the size of fast RAM is reduced by a factor of four. Degradation of image quality is less than 0.4 dB in PSNR.     

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     

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.     

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.     

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。     

等误差原则在进化算法优化矢量量化中的应用

文中利用进化算法优化矢量量化器设计,在选择后代码书矢量时,利用等误差原则选择获胜后代码书矢量.算法采用LBG算法作为基本聚类算法,利用所选后代码矢调整相应区域的父代码矢,减小各区域子误差,改善总的期望误差.试验证明了此方法的有效性,解决了LBG算法局部最优的局限,获取更接近全局最优的码书.     

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.     

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.     

Two fast nearest neighbor searching algorithms for image vectorquantization

In this paper, two efficient codebook searching algorithms for vector quantization (VQ) are presented. The first fast search algorithm utilizes the compactness property of signal energy on transform domain and the geometrical relations between the input vector and every codevector to eliminate those codevectors that have no chance to be the closest codeword of the input vector. It achieves a full search equivalent performance. As compared with other fast methods of the same kind, this algorithm requires the fewest multiplications and the least total times of distortion measurements. Then, a suboptimal searching method, which sacrifices the reconstructed signal quality to speed up the search of nearest neighbor, is presented. This algorithm performs the search process on predefined small subcodebooks instead of the whole codebook for the closest codevector. Experimental results show that this method not only needs less CPU time to encode an image but also encounters less loss of reconstructed signal quality than tree-structured VQ does     

应用神经网络的图像分类矢量量化编码

矢量量化作为一种有效的图像数据压缩技术，越来越受到人们的重视。设计矢量量化器的经典算法ＬＢＧ算法，由于运算复杂，从而限制了矢量量化的实用性。本文讨论了应用神经网络实现的基于边缘特征分类的矢量量化技术。它是根据人的视觉系统对图象的边缘的敏感性，应用模式识别技术，在对图像编码前，以边缘为特征对图像内容分类，然后再对每类进行矢量量化。除特征提取是采用离散余弦变换外，图像的分类和矢量量化都是由神经网络完成     

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。     

LBG算法中空胞腔优化处理研究

一个码书中空胞腔的码字越多,则LBG算法的量化效果就越不好,解码得到的信号失真就越大。在分析处理LBG算法中空胞腔的次邻域方法的基础上,针对次邻域法所存在的缺点,文中提出了一个新的空胞腔处理方法。利用仿真实验将其与传统的几种空胞腔处理方法进行比较分析,从类间离散度、矢量量化不均匀度、处理后所剩空胞腔数以及收敛性等性能指标都表明提出的改进方法达到减少量化失真,改善量化性能的目的。     

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.     

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     

Channel-optimized soft trellis waveform coding

We provide a new fuzzy relaxation trellis codebook search algorithm over noisy channels. The new algorithm solves the problems associated with the LBG algorithm in the sense of delivering relatively lower distortion configurations using short training sequences. Furthermore, the new approach is significantly less sensitive to the initialization process. The algorithm minimizes a weighted distortion measure averaged over both the source and the channel statistics. The weights are soft distortion-related reliability information, which are delivered by a soft trellis vector quantization algorithm (STVQ). The concept of soft compression is introduced by Haddad and Yongacoglu (see Proc. GLOBECOM, Rio de Janeiro, Brazil, Dec. 1999) in another paper, using the forward-backward symbol-MAP algorithm. The work introduced in this paper is an extension of the work established for the noiseless channel case by Haddad and Yongacoglu (see Proc. IEEE ICASSP, Istanbul, Turkey, June 2000) in yet another paper. Testing is performed using first- and second-order Gauss-Markov sources over several trellis structures, vector dimensions, and compression rates. Moreover, the robustness of the optimal configurations is tested under channel-mismatch conditions and compared with tandem coding systems.     

电子封装常用名称及术语汇集

电子封装常用名称及术语汇集下面，按英文字母顺序，汇集并解释了与目前ＬＳＩ（包括ＩＣ）正在采用的主要封装形式相关联的名称术语等。这些名称术语参考并引用了日本国内１２个半导体制造公司，其他国家７个半导体制造公司＊与ＬＳＩ封装相关的资料、日本电子机械工业会...     

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。