Digital implementation of hierarchical vector quantization

A formal methodology drives the design and realization of a digital very large-scale integration (VLSI) device supporting hierarchical vector quantization (HVQ) in computation-intensive coding applications. The hardware-oriented model-selection approach enhances the Minimum Description Length criterion with circuit-related aspects that allow consistent and efficient design. The resulting model parameters drive the subsequent realization in digital circuitry, which has first been implemented in field-programmable gate array (FPGA) technology to verify its correctness. The eventual VLSI realization results in an HVQ chip providing cost-effective, computationally efficient real-time performances. Real-world applications support the consistency of the vector quantization approach and the effectiveness of the HVQ device.

[1]  Nasser M. Nasrabadi,et al.  Image coding using vector quantization: a review , 1988, IEEE Trans. Commun..

[2]  Oscal T.-C. Chen,et al.  A VLSI neural processor for image data compression using self-organization networks , 1992, IEEE Trans. Neural Networks.

[3]  K. Kotani,et al.  A parallel vector-quantization processor eliminating redundant calculations for real-time motion picture compression , 2000, IEEE Journal of Solid-State Circuits.

[4]  W. Yang,et al.  VLSI hardware for example-based learning , 1997, IEEE Trans. Very Large Scale Integr. Syst..

[5]  Subhash C. Kwatra,et al.  A VLSI architecture for real-time image coding using a vector quantization based algorithm , 1992, IEEE Trans. Signal Process..

[6]  Rodolfo Zunino,et al.  Efficient training of neural gas vector quantizers with analog circuit implementation , 1999 .

[7]  Yong Liu,et al.  Unbiased estimate of generalization error and model selection in neural network , 1995, Neural Networks.

[8]  Sandro Ridella,et al.  K-winner machines for pattern classification , 2001, IEEE Trans. Neural Networks.

[9]  Simon Haykin,et al.  Neural network approaches to image compression , 1995, Proc. IEEE.

[10]  Bernabé Linares-Barranco,et al.  A real-time clustering microchip neural engine , 1996, IEEE Trans. Very Large Scale Integr. Syst..

[11]  J. Rissanen.  A UNIVERSAL PRIOR FOR INTEGERS AND ESTIMATION BY MINIMUM DESCRIPTION LENGTH , 1983 .

[12]  J. Rissanen.  Stochastic Complexity and Modeling , 1986 .

[13]  Michel Verleysen,et al.  Image compression by self-organized Kohonen map , 1998, IEEE Trans. Neural Networks.

[14]  David L. Neuhoff,et al.  On the asymptotic distribution of the errors in vector quantization , 1996, IEEE Trans. Inf. Theory.

[15]  Belle W. Y. Wei,et al.  A VLSI architecture for a real-time code book generator and encoder of a vector quantizer , 1994, IEEE Trans. Very Large Scale Integr. Syst..

[16]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[17]  Sandro Ridella,et al.  IAVQ-interval-arithmetic vector quantization for image compression , 2000 .

[18]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[19]  Rajeev Jain,et al.  An integrated circuit design for pruned tree-search vector quantization encoding with an off-chip controller , 1992, IEEE Trans. Circuits Syst. Video Technol..

[20]  Sandro Ridella,et al.  Empirical measure of multiclass generalization performance: the K-winner machine case , 2001, IEEE Trans. Neural Networks.

[21]  G. Cauwenberghs,et al.  A low-power CMOS analog vector quantizer , 1997 .

[22]  Oscal T.-C. Chen,et al.  VLSI systolic binary tree-searched vector quantizer for image compression , 1994, IEEE Trans. Very Large Scale Integr. Syst..

[23]  Rangasami L. Kashyap,et al.  A new sequential classifier using information criterion window , 1994, Pattern Recognit..

[24]  Jorma Rissanen,et al.  Stochastic Complexity in Statistical Inquiry , 1989, World Scientific Series in Computer Science.

[25]  Allen Gersho,et al.  Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.

[26]  Naonori Ueda,et al.  A new competitive learning approach based on an equidistortion principle for designing optimal vector quantizers , 1994, Neural Networks.

[27]  David B. Fogel.  An information criterion for optimal neural network selection , 1991, IEEE Trans. Neural Networks.

[28]  Rodolfo Zunino,et al.  Vector quantization for license-plate location and image coding , 2000, IEEE Trans. Ind. Electron..

[29]  B. Widrow,et al.  Statistical theory of quantization , 1996 .

[30]  Thomas Martinetz,et al.  'Neural-gas' network for vector quantization and its application to time-series prediction , 1993, IEEE Trans. Neural Networks.

[31]  H. Akaike.  A new look at the statistical model identification , 1974 .

[32]  Davide Anguita,et al.  Hyperparameter design criteria for support vector classifiers , 2003, Neurocomputing.