Fast adaptive scalar quantization for minimal mean squared error distortion in the high resolution case: extension of the boundary adaptation rule

Author

Van Hulle, Marc M.

Author_Institution

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

Volume

7

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

4365

Abstract

Recently the author (1993) introduced a new unsupervised competitive learning rule for adaptive scalar quantization. The rule, called boundary adaptation rule (BAR), directly adapts the boundary points demarcating the quantization intervals and minimizes the mean absolute error distortion. In this article the author extends the BAR concept towards the mean squared error distortion minimization in a high resolution case. The performance of this extended rule is shown for stationary as well as non-stationary input probability density functions, such as speech- and image signals. The rule yields near-optimal performance

Keywords

adaptive signal processing; image processing; least mean squares methods; minimisation; neural nets; probability; quantisation (signal); speech processing; unsupervised learning; adaptive scalar quantization; boundary adaptation rule; image signals; input probability density functions; minimal mean squared error distortion; neural networks; speech signals; unsupervised competitive learning rule; Acoustic sensors; Entropy; Intelligent sensors; Laboratories; Probability density function; Psychology; Quantization; Sensor phenomena and characterization; Signal resolution; Speech;

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.1994.374970

Filename

374970