two preprints on adaptive quantization

[Dominique Martinez]

The following two preprints on adaptive scalar quantization 
are available via anonymous ftp. 

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     Generalized Boundary Adaptation Rule for minimizing 
   r-th power law distortion in high resolution quantization. 

          Dominique Martinez and Marc M. Van Hulle
           
               (to appear in Neural Networks)
      

                       Abstract: 

A new generalized unsupervised competitive learning rule is introduced
for adaptive scalar quantization. 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) distortion with
substantially less iterations. In addition, since BAR_r does not
require generalized centroid estimation, as in Lloyd I, it is much
simpler to implement.
 
ftp laas.laas.fr
directory: pub/m2i/dmartine 
file: martinez.bar.ps.Z

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             A robust backward adaptive quantizer

             Dominique Martinez and Woodward Yang 

                   (to appear at NNSP'95)

                       Abstract: 

This paper describes an adaptive encoder/decoder for efficient
quantization of nonstationary signals. The system uses a robust
backward adaptive encoding method such that the adaptation of the
encoder and decoder is only determined by the transmitted codeword 
and does not require any additional side information. By incorporating 
a forgetting parameter, the quantizer is also robust to transmission 
errors and encoder/decoder mismatches. It is envisioned that practical 
applications of this algorithm can be used in the design of adaptive codecs 
(A/D and D/A converters) or as an efficient source coding algorithm for 
transmission of digitized speech.

ftp laas.laas.fr
directory: pub/m2i/dmartine
file: martinez.forget.ps.Z