Optimal weight assignment in recursive vector quantization

Image compression is a basic need for the progressive transmission or storage of image data via any media. For the effective processing of image data the coding standards were developed. The most commonly used standard in current scenario is the JPEG and JPEG-2000 coding. These standards have become the industry standards and are progressively been in use for almost all image processing applications. In the process of image coding various sub-processing operations occur which process the image blocks by transformation, quantization and encoding before transmitting or storing. In the process, quantization is observed to be a time consuming and is needed to be processed in high precision so as to achieve less processing error and also in reasonable time. The process of quantization has shown greater efficiency in vector quantization than earlier methods. Vector quantization is observed to be an efficient block based lossy compression technique. Due to simpler coding process and high compression ratio VQ are widely used. Predictive vector Quantization (PVQ) is commonly used memory based vector Quantization method. The main drawback with PVQ is the high computational complexity which limits PVQ to use in real time applications. Shorter coding time can be archived by reducing the number of code words or instructions, but the reconstructed image quality will degraded in the case of fewer code words. Hence, it is necessary to propose fast coding algorithms for PVQ. In this paper a modified version of the PVQ approach has been suggested in order to process faster, which ensue to be a compatible mechanism for real time image coding in JPEG based coding system. Keywords: Compression, quantization, PVQ encoder, mean-square-error, Discrete Cosine Transform.

A new predictive vector quantization (PVQ) technique capable of exploring the nonlinear dependencies in addition to the linear dependencies that exist between adjacent blocks (vectors) of pixels is introduced. The two components of the PVQ scheme, the vector predictor and the vector quantizer, are implemented by two different classes of neural networks. A multilayer perceptron is used for the predictive cornponent and Kohonen self-organizing feature maps are used to design the codebook for the vector quantizer. The multilayer perceptron uses the nonlinearity condition associated with its processing units to perform a nonlinear vector prediction. The second component of the PVQ scheme vector quantizes the residual vector that is formed by subtracting the output of the perceptron from the original input vector. The joint-optimization task of designing the two components of the PVQ scheme is also achieved. Simulation results are presented for still images with high visual quality.

Switched-predictive vector quantisation (SPVQ) has proven to be more efficient than conventional predictive vector quantisation (PVQ) to reduce the bit rate of LSF parameters in speech coding applications. We perform a comparison between different SPVQ schemes and we propose an extension of the SPVQ technique which outperforms previously reported PVQ and SPVQ systems. The proposed method consists of 4 vector quantisers (VQ) which are switched on the basis of the best performance criterion.

A new finite-state vector quantization scheme called jigsaw-puzzle vector quantization (JPVQ) is proposed to provide better image quality, especially in the low bit rate context. For low bit rate image coding with conventional finite-state vector quantization (FSVQ) techniques, image quality degrades due to error propagation from one state to the next. The proposed JPVQ algorithm exploits the four-step side-match prediction technique to optimize the spatial continuity of each encoded block to improve the coding performance and reduce the error propagation effect. In the proposed coding scheme, an input block can be encoded by the jigsaw-puzzle block, the dynamic codebook, or the super-codebook. It is demonstrated with experimental results that JPVQ performs significantly better than traditional FSVQ techniques.

A vector predictor is an integral part of the predictive vector quantization (PVQ) scheme. The performance of a predictor deteriorates as the vector dimension (block size) is increased. This makes it necessary to investigate new design techniques in order to design a vector predictor which gives better performance when compared to a conventional vector predictor. This paper investigates several neural network configurations which can be employed in order to design a vector predictor. The first neural network investigated in order to design the vector predictor is the multi-layer perceptron. The problem with multi-layer perceptron is the long convergence time which is undesirable when the on-line training of the neural network is required. Another neural network called functional link neural network has been shown to have fast convergence. The use of this network as a vector predictor is also investigated. The third neural network investigated is a recurrent type neural net. It is similar to the multi-layer perceptron except that a part of the predicted output is fed back to the hidden layer/layers in an attempt to further improve the current prediction. Finally, the use of a radial-basis function (RBF) network is also investigated for designing the vector predictor. The performances of the above mentioned neural network vector predictors are evaluated and compared with that of a conventional linear vector predictor.

In this paper, finite-state vector quantization (FSVQ) over noisy channels is studied. In particular a robust, time-recursive algorithm is proposed for reconstructing the output from a finite-state encoder, observed through a noisy channel. In contrast to an ordinary finite-state decoder, the proposed decoder exhibits graceful degradation of performance with increasing channel noise. We also consider the iterative optimization of encoder and decoder for designing channel optimized FSVQ. Simulation results based on Gauss-Markov source and additive white Gaussian noise channel are presented, and it is shown that robust FSVQ designed by methodology introduced here can outperform memory less channel optimized vector quantization at the same rate. Soft-decoding at the receiver, which provides an additional performance gain, is also considered.

Quantization of data is performed to reduce the bit rate required either for storage of data, or transmission between two or more communicators. Linear prediction (LP) is an efficient way to represent the short term spectrum of the speech signal. The line spectral frequency (LSF) transformation provides benefits over than linear prediction representations such as reflection coefficients, arc sine reflection coefficients, or log area ratios. Vector quantization of the filter parameters allows for a larger reduction in the bit rate to represent a set of parameters over scalar quantization. This reduction comes at the expense of using more computational complexity and greater amounts of storage. To reduce the burden of both storage and computation, various techniques and procedures have been developed. These techniques include full, multistage and split vector quantization, as well as the adaptive, variable dimension and finite state vector quantizers [1-8].

The generalized Lloyd algorithm (GLA), better known as the Linde-Buzo-Gray (LBG) algorithm, is the most widely used technique in classical vector quantization (VQ) for speech or image signal compression. However, the encoding complexity of the algorithm grows exponentially with the product of coding rate and vector dimension, which prohibits applying the technique to tasks with moderate to large encoding rates or vector dimensions. The authors present a new VQ scheme which overcomes the successive search coding computation of traditional techniques by using a quasi-parallel mapping technique and makes VQ practical for higher encoding rates and/or vector dimensions. Neural computing techniques are used to implement parallel encoding and decoding mappings in VQ and the developed algorithm was applied to quantizing Gauss-Markov processes and artificial data sources. Comparisons of performance with the LBG algorithm are given. >

음성코딩 시 성도는 Linear Predictive Coding (LPC) 계수를 이용해서 모델링 한다. 일반적으로 LPC 계수는 양자화와 선형보간 관점에서 유리한 Line Spectral Frequency (LSF) 파라미터로 변경하여 사용한다. 10차 이상의 다차원 LSF 데이터를 벡터 양자화를 이용하여 직접 코딩하게 되면 벡터 내 상관관계 (intra-frame correlation)를 모두 이용할 수 있으므로 rate-distortion 관점에서는 높은 효율을 기대할 수 있다. 하지만, 계산량과 메모리 요구량이 높아져서 실제 코딩 시스템에서는 사용할 수 없게 되므로, 차원을 나누어 압축하는 Split Vector Quantization (SVQ)이 이용된다. 또한, LSF 데이터는 과거 벡터와의 벡터 간 상관관계 (inter-frame correlation)가 높으므로, 이를 이용한 Predictive Split Vector Quantization (PSVQ)이 사용되고 있다. PSVQ는 SVQ 보다 높은 rate-distortion 성능을 보인다. 본 논문에서는 음성 저장 장치를 위한 최적의 PSVQ를 구현하기 위해서 다수의 과거 프레임 정보와의 벡터 간상관관계 (inter-frame correlation)를 고려한 Multi-Frame AR-model 기반 SVQ (MF-AR-SVQ)를 제안하였다. 기존 PSVQ와 비교해 보았을 때, MF-AR-SVQ는 계산량과 메모리 요구량의 큰 증가 없이, 평균 spectral distortion 관점에서 약 1비트의 성능 향상을 보였다. For speech coding, a vocal tract is modeled using Linear Predictive Coding (LPC) coefficients. The LPC coefficients are typically transformed to Line Spectral Frequency (LSF) parameters which are advantageous for linear interpolation and quantization. If multidimensional LSF data are quantized directly using Vector-Quantization (VQ), high rate-distortion performance can be obtained by fully utilizing intra-frame correlation. In practice, since this direct VQ system cannot be used due to high computational complexity and memory requirement, Split VQ (SVQ) is used where a multidimensional vector is split into multilple sub-vectors for quantization. The LSF parameters also have high inter-frame correlation, and thus Predictive SVQ (PSVQ) is utilized. PSVQ provides better rate-distortion performance than SVQ. In this paper, to implement the optimal predictors in PSVQ for voice storage devices, we propose Multi-Frame AR-model based SVQ (MF-AR-SVQ) that considers the inter-frame correlations with multiple previous frames. Compared with conventional PSVQ, the proposed MF-AR-SVQ provides 1 bit gain in terms of spectral distortion without significant increase in complexity and memory requirement.

The purpose of this research was to develop quantitative measures for the assessment of laryngeal function using speech and electroglottographic (EGG) data. We developed two procedures for the detection of laryngeal pathology: 1) a spectral distortion measure using pitch synchronous and asynchronous methods with linear predictive coding (LPC) vectors and vector quantization (VQ) and 2) analysis of the EGG signal using time interval and amplitude difference measures. The VQ procedure was conjectured to offer the possibility of circumventing the need to estimate the glottal volume velocity wave-form by inverse filtering techniques. The EGG procedure was to evaluate data that was "nearly" a direct measure of vocal fold vibratory motion and thus was conjectured to offer the potential for providing an excellent assessment of laryngeal function. A threshold based procedure gave 75.9 and 69.0% probability of pathological detection using procedures 1) and 2), respectively, for 29 patients with pathological voices and 52 normal subjects. The false alarm probability was 9.6% for the normal subjects.

Presents a simple but effective algorithm to speed up the codebook search in a vector quantization scheme when a MSE criterion is used. A considerable reduction in the number of operations is achieved. This algorithm was originally designed for image vector quantization in which the samples of the image signal (pixels) are positive, although it can be used with any positive-negative signal with only minor modifications.

A method for speaker independent isolated digit recognition based on modeling entire words as discrete probabilistic functions of a Markov process is described. Training is a three‐part process comprising conventional methods of linear prediction analysis and vector quantization of the LPCs followed by an algorithm [L. E. Baum, Inequalities 3, 1–8 (1972)] for estimating the parameters of a hidden Markov process. Recognition utilizes linear prediction and vector quantization steps prior to maximum likelihood classification based on the Viterbi algorithm [A. J. Viterbi, IEEE Trans. Inf. Theo. IT‐13, 260–269 (1967)]. After training based on a 1000‐token set, recognition experiments were conducted on a separate 1000‐token test set obtained from 100 new talkers. In this test a 3.5% error rate was observed which is comparable to that measured in an identical test of an LPC/DTW system [L. R. Rabiner et al., IEEE Trans. Acoust. Speech Signal Process. ASSP‐37, 336–349 (1979)]. The computational demand for recognition under the new system is reduced by a factor of approximately ten in both time and memory compared to that of the LPC/DTW system. Issues of selection of model structures, averaging techniques to obtain model stability, and methods of compensating for finite training set size are also discussed.

A novel modular coding paradigm is investigated using residual vector quantization (RVQ) with memory that incorporates a modular neural network vector predictor in the feedback loop. A modular neural network predictor consists of several expert networks that are optimized for predicting a particular class of data. The predictor also consists of an integrating unit that mixes the outputs of the expert networks to form the final output of the prediction system. The vector quantizer also has a modular structure. The proposed modular predictive RVQ (modular PRVQ) is designed by imposing a constraint on the output rate of the system. Experimental results show that the modular PRVQ outperforms simple PRVQ by as much as 1 dB at low bit rates. Furthermore, for the same peak signal-to-noise ratio (PSNR), the modular PRVQ reduces the bit rate by more than a half when compared to the JPEG algorithm.

Hierarchical Finite State Vector Quantization for Image Coding

A novel extrapolation selection finite state vector quantizer (ESFSVQ) has been devised. The state codebook selection algorithm is based on linear extrapolation predictions from the immediate upper and left neighboring blocks of each incoming source vector. It may be characterized as a vector quantizer with memory. The main accomplishment of ESFSVQ is to assure access to a large master codebook for quantizing purposes, and thus achieve high image quality, while utilizing the low bit rate of a small state codebook, for the purpose of specifying the block label. A master codebook of 4*4 blocks of size 256, 512 or 1024, state codebook sizes of 32, 64, or 128 vectors, was employed in implementing ESFSVQ. The results have shown that high image quality can be achieved, upon image reconstruction, at coding bit rates of 0.30-0.45 b/pixel. >