Generalized boundary adaptation rule for minimizing rth power law distortion in high resolution quantization
نویسندگان
چکیده
A new generalized unsupervised competitive learning rule is introduced for adaptive scalar quantization. The rule, called generalized Boundary Adaptation Rule (BARr), minimizes r-th power law distortion Dr in the high resolution case. It is shown by simulations that a fast version of BARr outperforms generalized Lloyd I in minimizing D1 (mean absolute error) and D2 (mean squared error) distortion with substantially less iterations. In addition, since BARr does not require generalized centroid estimation, as in Lloyd I, it is much simpler to implement.
منابع مشابه
Generalized Boundary Adaptation Rule for minimizingr - th power law distortion in case of high resolution quantization
A new generalized unsupervised competitive learning rule is introduced for adaptive scalar quan-tization. The rule, called generalized Boundary Adaptation Rule (BAR r), minimizes r-th power law distortion D r in the high resolution case. It is shown by simulations that a fast version of BAR r outperforms generalized Lloyd I in minimizing D 1 (mean absolute error) and D 2 (mean squared error) di...
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ورودعنوان ژورنال:
- Neural Networks
دوره 8 شماره
صفحات -
تاریخ انتشار 1995