An Algorithm for the Design of Labeled-Transition Finite-State Vector Quantizers

Dunham, M.; Gray, Robert M.

doi:10.1109/tcom.1985.1096198

Cited by 95 publications

(13 citation statements)

References 11 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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show abstract

“…Therefore finite-state VQ [4] (FSVQ) has been proposed. It exploits the statistical correlation between neighbouring blocks to reduce the bit rate (BR) of the compressed image.…”

Section: Introductionmentioning

confidence: 99%

“…We use (4) to compute the difference between dis 1 and dis 2 diff = dis 1 − dis 2 (4) (i) If the value of diff is smaller than the pre-defined threshold TH diff , ESMVQ decides that the codeword CW bm1 is the closest codeword for the current block X in the codebook. ESMVQ determines CW bm1 to be the encoding result.…”

mentioning

confidence: 99%

Enhanced side match vector quantisation based on constructing complementary state codebook

Pan

et al. 2015

IET Image Processing

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Side match vector quantisation (SMVQ) technique has been widely used in a lot of image compression and data hiding applications. It can effectively decrease the bit rate (BR) but the encoding visual quality of the image using SMVQ is generally poor because the correlation among neighbouring image blocks is still low. In this study, the authors propose a new enhanced SMVQ (ESMVQ) by introducing the concept of complementary state codebook (CSC) to improve the visual quality of SMVQ. Encoding input vectors by using CSC, ESMVQ can achieve almost the same encoding visual quality as using the conventional vector quantisation. As a result, the encoding visual quality of image can be significantly improved by exploiting the power of CSC. Experimental results show that the improvement of proposed ESMVQ method over SMVQ is up to 4.677 dB at a similar BR for image Lena when the main codebook size is 1024.

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“…Although the ordinary VQ scheme exploits the statistical redundancy within a vector and yields an acceptable performance at low bit rates, the finite-state vector quantization (FSVQ) schemes can improve performance over the ordinary VQ by exploiting the correlation between neighboring vectors within an image [5][6][7][8][9][10][11][12]. The encoding state of the current input vector is decided by the previous encoded neighboring vectors.…”

Section: Introductionmentioning

confidence: 99%

A New Side-Match Finite-State Vector Quantization Using Neural Networks for Image Coding

Huang

Chang

2002

Journal of Visual Communication and Image Representation

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The side-match finite-state vector quantization (SMVQ) schemes improve performance over the vector quantization by exploiting the neighboring vector correlations within the image. In this paper, we propose a neural network side-match finite-state vector quantization (NN-SMVQ) scheme that combines the techniques of neural network prediction and the SMVQ coding method. In our coding scheme, the multilayer perceptron network is used to improve the accuracy of side-match prediction by utilizing the property of the neural network nonlinear prediction. The NN-SMVQ scheme not only has the advantages of the SMVQ scheme but also improves the coded image quality. Experimental results are given and comparisons are made using our NN-SMVQ coding scheme and some other coding techniques. In the experiments, our NN-SMVQ coding scheme achieves the better visual quality about edge region and the best PSNR performance at nearly the same bit rate. This new NN-SMVQ scheme is also simple and efficient for the hardware design. Moreover, the new scheme does not adversely affect other useful functions provided by the conventional SMVQ scheme. C 2002 Elsevier Science (USA)

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An Algorithm for the Design of Labeled-Transition Finite-State Vector Quantizers

Cited by 95 publications

References 11 publications

On Optimal Coding of Hidden Markov Sources

On Optimal Coding of Hidden Markov Sources

Enhanced side match vector quantisation based on constructing complementary state codebook

A New Side-Match Finite-State Vector Quantization Using Neural Networks for Image Coding

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