Constrained-storage vector quantization with a universal codebook

Many image compression techniques require the quantization of multiple vector sources with significantly different distributions. With vector quantization (VQ), these sources are optimally quantized using separate codebooks, which may collectively require an enormous memory space. Since storage is limited in most applications, a convenient way to gracefully trade between performance and storage is needed. Earlier work addressed this problem by clustering the multiple sources into a small number of source groups, where each group shares a codebook. We propose a new solution based on a size-limited universal codebook that can be viewed as the union of overlapping source codebooks. This framework allows each source codebook to consist of any desired subset of the universal code vectors and provides greater design flexibility which improves the storage-constrained performance. A key feature of this approach is that no two sources need be encoded at the same rate. An additional advantage of the proposed method is its close relation to universal, adaptive, finite-state and classified quantization. Necessary conditions for optimality of the universal codebook and the extracted source codebooks are derived. An iterative design algorithm is introduced to obtain a solution satisfying these conditions. Possible applications of the proposed technique are enumerated, and its effectiveness is illustrated for coding of images using finite-state vector quantization, multistage vector quantization, and tree-structured vector quantization.     

采用通用码书实现存储受限矢量量化

针对图像源矢量量化（VQ）过程中码书储需很大空间面空间又有限的矛盾,提出了一种采用通用码书实现存储受限矢量量化（CSVQ）的解决方案。各种图像源（矢量源）所用的子码书都可从这个通用码书中提取出来,而这些子码书之间存在交集,大大节约了所需存储空间,并且,各种矢量源可以以不同的速率编码。     

Optimization of requantization codebook for vector quantization.

Requantization is a key technology for reducing the bit rate of a previously compressed data. When recompression ratio is high, the requantizer may cause unacceptable quality degradation. To improve the quality of the requantized image, an optimization scheme for the requantization codebook has been proposed. The proposed scheme constructs an optimal requantization codebook in an iterative manner for a given original quantization codebook of transmitter. The construction of codebook is iteratively repeated until they reach a local optimum solution. Our approach can be applied not only to the scalar quantization, but to any method which employs vector quantization-based system. Simulation results show that the optimized system based on the proposed algorithm outperforms the conventional system which is made without consideration of requantization. The proposed algorithm enables a reliable image communication over heterogeneous networks.     

Enhanced multistage vector quantization by joint codebook design

Multistage vector quantization (MSVQ) can achieve very low encoding and storage complexity in comparison to unstructured vector quantization. However, the conventional stage-by-stage design of the codebooks in MSVQ is suboptimal with respect to the overall performance measure. The authors introduce an algorithm for the joint design of the stage codebooks to optimize the overall performance. The performance improvement, although modest, is achieved with no effect on encoding or storage complexity and only a slight increase in design effort     

Accelerating the optimization of requantization codebook for vector quantization.

Requantization is an important technique for variable-rate data compression and communication and optimal design for requantization is key to guaranteeing the minimization of the requantization noise. Recently, an iterative optimization algorithm for designing the requantization codebook was proposed. We develop further results regarding the acceleration of the algorithm and a better understanding of the problem of requantization. In particular, our result shows that the quantization and requantization processes can be decoupled into two nearly independent sequential stages, and that requantization does not need the original training vectors. All these features pave the way toward real-time variable-rate data compression and transmission over heterogeneous networks. Simulation results support the theory and the advantages of our method.     

Adaptive vector quantization with codebook updating based on locality and history

We propose two techniques that are applicable to any adaptive vector quantization (AVQ) systems. The first one is called the locality-based codebook updating: when performing a codebook updating, we update the operational codebook using not only the current input vector but also the codewords at all positions within a selected neighboring area (called the locality), while the operational codebook is organized in a "cache" manner. This technique is rationalized by the high correlation cross neighboring vectors that facilitates a more efficient coding of the indices of the codewords chosen from the codebook. The second technique is called the history aid, which makes use of the information of previously coded vectors to quantize the current input vector if it is used to update the operational codebook. A more effective AVQ system is obtained by combining together the history aid and the locality-based updating. Extensive simulations are carried out to demonstrate the improved results achieved by our AVQ systems. Particularly, when the operational codebook size is relatively small, the improvement over a benchmark AVQ system - the generalized threshold replenishment (GTR) - is drastic. For example, when the size is 32, testing on a nonstationary signal (containing frames from different video sequences, ordered in the concatenating or interleaving format) shows that the combination of history aid and locality-based updating offers more than 4 dB gain over GTR at 0.5 bpp.     

Wavelet-based ECG compression using dynamic vector quantization with tree codevectors in single codebook

In this paper, we propose a novel vector quantizer (VQ) in the wavelet domain for the compression of electrocardiogram (ECG) signals. A vector called tree vector (TV) is formed first in a novel structure, where wavelet transformed (WT) coefficients in the vector are arranged in the order of a hierarchical tree. Then, the TVs extracted from various WT subbands are collected in one single codebook. This feature is an advantage over traditional WT-VQ methods, where multiple codebooks are needed and are usually designed separately because numerical ranges of coefficient values in various WT subbands are quite different. Finally, a distortion-constrained codebook replenishment mechanism is incorporated into the VQ, where codevectors can be updated dynamically, to guarantee reliable quality of reconstructed ECG waveforms. With the proposed approach both visual quality and the objective quality in terms of the percent of root-mean-square difference (PRD) are excellent even in a very low bit rate. For the entire 48 records of Lead II ECG data in the MIT/BIH database, an average PRD of 7.3% at 146 b/s is obtained. For the same test data under consideration, the proposed method outperforms many recently published ones, including the best one known as the set partitioning in hierarchical trees.     

Unitary space vector quantization codebook design for spatial correlated limited feedback MIMO system

In the transmitting, beamforming, and receiving combing （TBRC） MIMO system, a codebook based feedback strategy is usually used to provide the transmitter with the beamforming vector. The adopted codebook affects the system performance considerably. Therefore, the codebook design is a key technology in the TBRC MIMO system. In this article, the unitary space vector quantization （USVQ） codebook design criterion is proposed to design optimal codebooks for various spatial correlated MIMO channels. And the unitary space K-mean （USK） codebook generating algorithm is provided to generate the USVQ codebooks. Simulations show that the capacities of the feedback based TBRC systems using USVQ codebooks are very close to those of the ideal cases.     

Effect of ordering the codebook on the efficiency of the partialdistance search algorithm for vector quantization

Recently, C.D. Bei and R.M. Gray (1985) used a partial distance search algorithm that reduces the computational complexity of the minimum distortion encoding for vector quantization. The effect of ordering the codevectors on the computational complexity of the algorithm is studied. It is shown that the computational complexity of this algorithm can be reduced further by ordering the codevectors according to the sizes of their corresponding clusters     

Alphabet-constrained vector quantization

Alphabet-constrained rate-distortion theory is extended to coding of sources with memory. Two different cases are considered: when only the size of the codebook is constrained and when the codevector values are also held fixed. For both cases, nth-order constrained-alphabet rate-distortion functions are defined and a convergent algorithm for their evaluation is presented. Specific simulations using AR(1) sources show that performance near the rate-distortion bound is possible using a reproduction alphabet consisting of a small number of codevectors. It is also shown that the additional constraint of holding the codevector values fixed does not degrade performance of the coder in relation to the size-only constrained case. This observation motivates the development of a fixed-codebook vector quantizer, called the alphabet- and entropy-constrained vector quantizer, the performance of which is comparable to the entropy-constrained vector quantizer. A number of examples using an AR(1) and a speech source are presented to corroborate the theory     

Trellis-coded vector quantization

Trellis-coded quantization is generalized to allow a vector reproduction alphabet. Three encoding structures are described, several encoder design rules are presented, and two design algorithms are developed. It is shown that for a stationary ergodic vector source, if the optimized trellis-coded vector quantization reproduction process is jointly stationary and ergodic with the source, then the quantization noise is zero-mean and of a variance equal to the difference between the source variance and the variance of the reproduction sequence. Several examples illustrate the encoder design procedure and performance     

Adaptive vector quantization

Annals of Telecommunications - In adaptive quantization, the parameters of a quantizer are updated during real-time operation based on observed information regarding the statistics of the signal...     

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     

基于矢量量化的层次分形编码方法

文中提出了一种新的分形图像压缩方法,该方法将矢量量化的概念应用于分形块编码中,对图像的平缓区进行矢量量化的线性组合编码,对图像的丰富细节区用分形编码,并且在分形编码时,采取了层次处理。实验表明,与基本的分形块编码方法相比,本文提出的矢量量化层次分形编码方法在保证一定的重建图像质量下,使图像的压缩比有了明显的提高,并且大大提高了编码和解码速度。     

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.     

A study of vector quantization for noisy channels

Several issues related to vector quantization for noisy channels are discussed. An algorithm based on simulated annealing is developed for assigning binary codewords to the vector quantizer code-vectors. It is shown that this algorithm could result in dramatic performance improvements as compared to randomly selected codewords. A modification of the simulated annealing algorithm for binary codeword assignment is developed for the case where the bits in the codeword are subjected to unequal error probabilities (resulting from unequal levels of error protection). An algorithm for the design of an optimal vector quantizer for a noisy channel is briefly discussed, and its robustness under channel mismatch conditions is studied. Numerical results for a stationary first-order Gauss-Markov source and a binary symmetric channel are provided. It is concluded that the channel-optimized vector quantizer design algorithm, if used carefully, can result in a fairly robust system with no additional delay. The case in which the communication channel is nonstationary (as in mobile radio channels) is studied, and some preliminary ideas for quantizer design are presented     

Symmetric trellis-coded vector quantization

We present here design techniques for trellis-coded vector quantizers with symmetric codebooks that facilitate low-complexity quantization as well as partitioning into equiprobable sets for trellis coding. The quantization performance of this coder on the independently identically distributed (i.i.d.) Laplacian source matches the performance of trellis-based scalar-vector quantization (TB-SVQ), but requires less computational complexity