Title :
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
fDate :
27 Jun-2 Jul 1994
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;
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.1994.374970
