Finite-state vector quantization for waveform coding

Foster, John Burt; Gray, Robert M.; Dunham, M.

doi:10.1109/tit.1985.1057035

Cited by 243 publications

(81 citation statements)

References 22 publications

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“…An alternative to predictive coding is encoding by a finite-state quantizer (see eg. [3,4]). A finite-state quantizer is a finite-state machine used for data compression: each successive source sample is quantized using a quantizer code book that depends on the encoder state.…”

Section: Introductionmentioning

confidence: 99%

On Optimal Coding of Hidden Markov Sources

Salehifar¹,

Akyol²,

Viswanatha³

et al. 2014

2014 Data Compression Conference

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The hidden Markov model (HMM) is widely used to model processes in several real world applications, including speech processing and recognition, image understanding and sensor networks. A problem of concern is that of quantization of the sequence of observations generated by an HMM, which is referred as a hidden Markov source (HMS). Despite the importance of the problem, and the well-defined structure of the process, there has been very limited work addressing the optimal quantization of HMS, and conventional approaches focus on optimization of parameters of known quantization schemes. This paper proposes a method that directly tracks the state probability distribution of the underlying source and optimizes the encoder structure according to the estimated HMS status. Unlike existing approaches, no stationarity assumption is needed, and code parameters are updated on the fly: with each observation, both the encoder and the decoder refine the estimated probability distribution over the states. The main approach is then specialized to a practical variant involving switched quantizers, and an algorithm that iteratively optimizes the quantizer codebooks is derived. Numerical results show superiority of the proposed approach over prior methods. IntroductionThe Hidden Markov model is a discrete-time finite state Markov chain observed through a memoryless channel. The random process consisting of the sequence of observations is referred to as a hidden Markov source (HMS). Markov chains are common models for information sources with memory and memoryless channel is among the simplest communication models. HMMs are widely used in image understanding and speech recognition [1], source coding [2], communications, information theory, economics, robotics, computer vision and several other disciplines. Note that most signals modeled as Markov process are in fact captured by imperfect sensors and are hence contaminated with noise, i.e., the resulting sequence is an HMS. Motivated by its modeling capability of practical sources with memory, in this paper we consider optimal quantization of the HMS . One conventional approach to design quantizers for the HMS is to employ predictive coding techniques (such as DPCM) to exploit time correlations, followed by standard scalar quantization of the residual. However, any direct prediction method cannot fully exploit the structure of the source process, i.e., the underlying Markov process. Indeed, even if the underlying process is first order Markov (depends on the past only through the previous sample), the HMS is not Markov, i.e, all prior samples are needed to optimally predict the current sample. But a naive infinite order prediction is clearly of impractical complexity. An alternative to predictive coding is encoding by a finite-state quantizer (see eg. [3,4]

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Section: Introductionmentioning

confidence: 99%

On Optimal Coding of Hidden Markov Sources

Salehifar¹,

Akyol²,

Viswanatha³

et al. 2014

2014 Data Compression Conference

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“…where t r represents the r th value of the threshold and n is the number of quantization levels and four quantizing levels a, b, c and d are calculated using the equation (9). Quantizer values are optimized.…”

Section: E Minimum Mean Square Error (Mmse) Methodsmentioning

confidence: 99%

Multilevel Compression Scheme using Vector Quantization for Image Compression

Vimala¹,

Abidha²,

Uma³

2011

IJCEE

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Abstract-In this paper, we have proposed a multi level compression scheme, in which the initial codebook and the Index Map that are generated using the normal Vector Quantization are compressed in three levels. The Minimum Mean Square method that is adopted in Block Truncation Coding is used to compress the codebook in first level with some improvements. The interpolative method is used to perform second level of compression to the codebook. Finally the Search Order Coding method is used to compress the Index Map without loss of image data. The experimental results show that there is a significant improvement in bit reduction without loosing much of image data.

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“…The response time is very important factor for real time application [1]. M any type of VQ, such as classified VQ [9], [10], address VQ [9], [11], finite state VQ [9], [12], side match VQ [9], [13], mean-removed classified VQ [9], [14], and predictive classified VQ [9], [15], have been used for various purposes Vector Quantization (VQ) is a compression technique based on grouping blocks of information based on the similarity of their values. There is a loss of quality while using VQ, but this is duly compensated by the significant savings achieved by this compression method.…”

Section: Vector Quantization:-mentioning

confidence: 99%

"Performance Comparison of LBG, KPE, KFCG and KMCG for Global Codebook Technique"

Kekre¹,

Tanuja²,

Raut³

et al. 2011

IJCA

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Vector quantization is a classical quantization technique from signal processing which allows the modeling of probability density functions by the distribution of prototype vectors. It was originally used for data compression. It works by dividing a large set of points (vectors) into groups having approximately the same number of points closest to them. Each group is represented by its centroid point, as in clustering algorithms.Vector Quantization is a technique of compressing data based on grouping blocks having similar data. These blocks are called Code Vectors and all the code vectors grouped together is called a Codebook. The key to VQ data compression is a good codebook. In order to reduce bandwidth overhead it is necessary to generate Global Codebook for a particular class of images. Otherwise local codebook has to be transferred every time before the transmission of image. In this paper various global codebook generation algorithms for vector quantization for color images are presented.

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Finite-state vector quantization for waveform coding

Cited by 243 publications

References 22 publications

On Optimal Coding of Hidden Markov Sources

On Optimal Coding of Hidden Markov Sources

Multilevel Compression Scheme using Vector Quantization for Image Compression

"Performance Comparison of LBG, KPE, KFCG and KMCG for Global Codebook Technique"

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