Adaptive compandor design using the boundary adaptation rule

Author

Van Hulle, Marc M. ; Martinez, Dominique

Author_Institution

Lab. voor Neuro- en Psychofysiologie, Katholieke Univ., Leuven, Belgium

Volume

6

fYear

1994

fDate

27 Jun- 2 Jul 1994

Firstpage

3597

Abstract

In our previous paper (1993), we introduced a novel unsupervised learning rule for scalar quantization, called the boundary adaptation rule (BAR). Adaptive quantizers were built using the maximization of information-theoretic entropy as a design criterion. In this paper, we show that BAR can also be used for designing quantizers by minimizing the mean square error distortion due to quantization. For this purpose, the adaptive histogram with equal bin counts assessed by BAR is used as a density estimator to build an optimal compandor function

Keywords

adaptive equalisers; compandors; quantisation (signal); unsupervised learning; adaptive compandor; adaptive histogram; adaptive quantizers; boundary adaptation rule; density estimator; mean square error distortion; quantization; unsupervised learning rule; Entropy; Histograms; Integral equations; Laboratories; Mean square error methods; Oral communication; Quantization; Signal processing; Unsupervised learning; Yield estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374915

Filename

374915