Title :
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
fDate :
27 Jun- 2 Jul 1994
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;
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
DOI :
10.1109/ICNN.1994.374915
