Robust vector quantization by a linear mapping of a block code

In this paper we propose a novel technique for vector quantizer design where the reconstruction vectors are given by a linear mapping of a binary block code (LMBC). The LMBC framework provides a relation between the index bits and the reconstruction vectors through mapping properties. We define a framework, show its flexibility, and give optimality conditions. We consider source optimized vector quantization (VQ), where the objective is to directly obtain a VQ with inherent good channel robustness properties. Several instructive theoretical results and properties of the distortion experienced due to channel noise are demonstrated. These results are used to guide the design process. Both optimization algorithms and a block code selection procedure are devised. Experimental results for Gauss-Markov sources show that quantization performance close to an unconstrained VQ is obtained with a short block code which implies a constrained VQ. The resulting VQs have better channel noise robustness than conventional VQs designed with the generalized Lloyd algorithm (GLA) and splitting initialization, even when a post-processing index assignment algorithm is applied to the GLA-based VQ. We have, thus, demonstrated a unique method for direct design resulting in an inherent good index assignment combined with small losses in quantization performance     

Novel fuzzy reinforced learning vector quantisation algorithm and its application in image compression

A new approach to the design of optimised codebooks using vector quantisation (VQ) is presented. A strategy of reinforced learning (RL) is proposed which exploits the advantages offered by fuzzy clustering algorithms, competitive learning and knowledge of training vector and codevector configurations. Results are compared with the performance of the generalised Lloyd algorithm (GLA) and the fuzzy K-means (FKM) algorithm. It has been found that the proposed algorithm, fuzzy reinforced learning vector quantisation (FRLVQ), yields an improved quality of codebook design in an image compression application when FRLVQ is used as a pre-process. The investigations have also indicated that RL is insensitive to the selection of both the initial codebook and a learning rate control parameter, which is the only additional parameter introduced by RL from the standard FKM     

Linearly constrained generalized Lloyd algorithm for reducedcodebook vector quantization

As linearly constrained vector quantization (LCVQ) is efficient for block-based compression of images that require low complexity decompression, it is a “de facto” standard for three-dimensional (3-D) graphics cards that use texture compression. Motivated by the lack of an efficient algorithm for designing LCVQ codebooks, the generalized Lloyd (1982) algorithm (GLA) for vector quantizer (VQ) codebook improvement and codebook design is extended to a new linearly constrained generalized Lloyd algorithm (LCGLA). This LCGLA improves VQ codebooks that are formed as linear combinations of a reduced set of base codewords. As such, it may find application wherever linearly constrained nearest neighbor (NN) techniques are used, that is, in a wide variety of signal compression and pattern recognition applications that require or assume distributions that are locally linearly constrained. In addition, several examples of linearly constrained codebooks that possess desirable properties such as good sphere packing, low-complexity implementation, fine resolution, and guaranteed convergence are presented. Fast NN search algorithms are discussed. A suggested initialization procedure halves iterations to convergence when, to reduce encoding complexity, the encoder considers the improvement of only a single codebook for each block. Experimental results for image compression show that LCGLA iterations significantly improve the PSNR of standard high-quality lossy 6:1 LCVQ compressed images     

噪声信道中基于进化算法的矢量量化器的设计

本文提出了一个基于进化算法的信道最优矢量量化器（COVQ）设计算法。该算法在给定信道状态模型和存在信道噪声的情况下，可以有效地提高矢量量化器的性能，实现了信道最优矢量量化器的设计。与目前常用的码书设计算法比较，实验结果表明该算法可获得比传统算法更高的性能增益。     

Fuzzy channel-optimized vector quantization

A novel fuzzy clustering algorithm for the design of channel-optimized source coding systems is presented in this letter. The algorithm, termed fuzzy channel-optimized vector quantizer (FCOVQ) design algorithm, optimizes the vector quantizer (VQ) design using a fuzzy clustering process in which the index crossover probabilities imposed by a noisy channel are taken into account. The fuzzy clustering process effectively enhances the robustness of the performance of VQ to channel noise without reducing the quantization accuracy. Numerical results demonstrate that the FCOVQ algorithm outperforms existing VQ algorithms under noisy channel conditions for both Gauss-Markov sources and still image data     

Residual vector quantization using a multilayer competitive neuralnetwork

This paper presents a new technique for designing a jointly optimized residual vector quantizer (RVQ). In conventional stage-by-stage design procedure, each stage codebook is optimized for that particular stage distortion and does not consider the distortion from the subsequent stages. However, the overall performance can be improved if each stage codebook is optimized by minimizing the distortion from the subsequent stage quantizers as well as the distortion from the previous stage quantizers. This can only be achieved when stage codebooks are jointly designed for each other. In this paper, the proposed codebook design procedure is based on a multilayer competitive neural network where each layer of this network represents one stage of the RVQ. The weight connecting these layers form the corresponding stage codebooks of the RVQ. The joint design problem of the RVQ's codebooks (weights of the multilayer competitive neural network) is formulated as a nonlinearly constrained optimization task which is based on a Lagrangian error function. This Lagrangian error function includes all the constraints that are imposed by the joint optimization of the codebooks. The proposed procedure seeks a locally optimal solution by iteratively solving the equations for this Lagrangian error function. Simulation results show an improvement in the performance of an RVQ when designed using the proposed joint optimization technique as compared to the stage-by-stage design, where both generalized Lloyd algorithm (GLA) and the Kohonen learning algorithm (KLA) were used to design each stage codebook independently, as well as the conventional joint-optimization technique     

A vector quantizer for the Laplace source

The low complexity, nearly optimal vector quantizer (VQ) is a generalization of T. R. Fischer's (1986) pyramid VQ and is similar in structure to the unrestricted polar quantizers previously presented for the independent Gaussian source. An analysis of performance is presented with results for both the product code pyramid VQ and the unrestricted version. This analysis, although asymptotic in nature, helps to demonstrate the performance advantages of the VQ. Implementation issues of the VQ are discussed. Nonasymptotic results are considered. In particular, the author presents an approximate design algorithm for finite bit rate and demonstrates the usefulness of this VQ through several example designs with Monte Carlo simulations of performance. For the restricted form (the pyramid VQ), the author provides further implementational information and low dimension analytical results     

An efficient vector quantizer providing globally optimal solutions

This paper presents a new approach in vector quantization that is designed for clustering or source coding. It incorporates both the capability of fast convergence from a monotonically descending algorithm and provides a globally optimal solution by a random optimization technique. Thus, it benefits from properties of deterministic and stochastic search. Comprehensive experiments demonstrate that the new algorithm actually assimilated the advantages of the both components. It may be therefore regarded as an accelerated global optimization method whose convergence is theoretically proved. According to the complexity of the quantization problem, the convergence rate is shown (numerically) to approach that of a coordinate descent algorithm, which is an iterative updating of a single codevector at a time (generalized Lloyd algorithm GLA, i.e., K-means). The new method is investigated and compared with GLA and a globally operating stochastic relaxation technique. The comparison was made with respect to quality, reliability, and efficiency and applied to four categories of data: an easy to grasp example, patterns derived from the EEG, Gauss-Markov, and image sources     

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     

宽带随机混沌雷达高速目标速度估计与成像

随机混沌具有真随机性、对初值敏感、易于产生和控制等特点,频率步进信号易于工程实现和处理,结合两者的优势,提出了一种载频随机步进的随机混沌信号(RSCFSCS)模型,用于高速目标的速度估计和距离维高分辨成像。首先,通过非周期函数激励非线性系统,产生不可预测的随机混沌信号(SCS),经频率调制后用作基带子脉冲。同时,将SCS通过映射变换得到跳频编码(FHC),用来决定调频脉冲串的载频步进。RSCFSCS 速度估计包括粗搜索和精搜索,粗搜索采用固定步长,保证速度偏差小于速度分辨单元,而精搜索采用黄金分割搜索算法可得到精确的速度估计。最后,子脉冲经相干合成形成宽带信号,实现高分辨距离成像。数值仿真表明提出的信号模型和处理算法性能良好。     

矢量量化在语音波形编码中的应用

矢量量化(VQ)技术是近几年发展起来的一种高效数据压缩技术.本文介绍了VQ技术的发展历史、现状和它的基本原理,较为详细地讨论了基本矢量量化器的实用设计方法——LBG算法,并对原有的LBG算法进行了改进,给出了实验结果.     

Gain-Adaptive Vector Quantization with Application to Speech Coding

The generalization of gain adaptation to vector quantization (VQ) is explored in this paper and a comprehensive examination of alternative techniques is presented. We introduce a class of adaptive vector quantizers that can dynamically adjust the "gain" or amplitude scale of code vectors according to the input signal level. The encoder uses a gain estimator to determine a suitable normalization of each input vector prior to VQ encoding. The normalized vectors have reduced dynamic range and can then be more efficiently coded. At the receiver, the VQ decoder output is multiplied by the estimated gain. Both forward and backward adaptation are considered and several different gain estimators are compared and evaluated. Gain-adaptive VQ can be used alone for "vector PCM" coding (i.e., direct waveform VQ) or as a building block in other vector coding schemes. The design algorithm for generating the appropriate gain-normalized VQ codebook is introduced. When applied to speech coding, gain-adaptive VQ achieves significant performance improvement over fixed VQ with a negligible increase in complexity.     

误差敏感竞争性学习算法

本文基于等误差准则提出了一种适用于矢量量化技术的新型码书设计算法。实验表明此算法优于现存算法。为解决初始码书赋值问题，本文提出了自生成自组织神经网络方法。实验表明此算法加速了算法的收敛速度，提高了算法的性能     

随机混沌感知矩阵及其在成像雷达中的应用

根据压缩感知理论提出了一种适用于成像雷达的新算法,在成像目标分布满足稀疏性前提下,利用发射的随机混沌序列( SCS )形成卷积矩阵,然后通过随机行抽取构造随机感知矩阵( SC-SM)。给出了完整的算法实现框架,从理论上证明了SCS的随机性和统计独立性以及SCSM的有限等距性( Restricted Isometry Property,RIP)。仿真结果验证了算法的有效性,同时分析了影响算法性能的主要因素。与匹配滤波法相比,所提算法重构误差小,输出旁瓣低。 SCSM与其他随机矩阵具有相同的性能,然而,SCSM容易在硬件上实现,且更适用于要求保密性高和抗干扰能力强的场合。     

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.     

Compression of color facial images using feature correctiontwo-stage vector quantization

A feature correction two-stage vector quantization (FC2VQ) algorithm was previously developed to compress gray-scale photo identification (ID) pictures. This algorithm is extended to color images in this work. Three options are compared, which apply the FC2VQ algorithm in RGB, YCbCr, and Karhunen-Loeve transform (KLT) color spaces, respectively. The RGB-FC2VQ algorithm is found to yield better image quality than KLT-FC2VQ or YCbCr-FC2VQ at similar bit rates. With the RGB-FC2VQ algorithm, a 128x128 24-b color ID image (49152 bytes) can be compressed down to about 500 bytes with satisfactory quality. When the codeword indices are further compressed losslessly using a first order Huffman coder, this size is further reduced to about 450 bytes.     

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

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.     

一种基于改进的SOFM神经网络的图像无损压缩方法

在介绍矢量量化和自组织特征映射神经网(SOFM)的基础上,针对SOFM算法的特点对其进行了几个方面的改进,提高了SOFM网络的性能。采用改进后的基于SOFM的矢量量化技术对图像进行无损压缩编码,码书设计时间减少了约70%,图像效果、编码质量均有所提高,实验结果表明了本算法的压缩比比传统的差值编码(DPCM)无损压缩最高可提升40%,证明了算法的有效性。     

基于VC6.0开发平台的矢量量化算法实现及优化

矢量量化是一种高效的有损压缩技术,但其存在编码算法实现实时性不高的问题.为了提高编码算法在PC机上的执行效率,文中从现有的成熟矢量量化有效算法(基于不等式删除准则)入手,针对PC机上intel CPU的工作特点,分析了矢量量化算法优化的特点,提出了采用MMX指令等有效的优化方法.