In some applications, such as data transmission, signal
variance is unknown but constant. In such cases, adaptive
quantizers using local variance estimation algorithms are
not appropriate for the signal quantizations. The most
suitable algorithms for this situation are those which
learn the input signal variance.
This work examines four variance learning algorithms for
application in adaptive quantization. One of them,
proposed by A. Gersho and D. J. Goodman, is a stochastic
approximation algorithm which converges with probability
one, when applied to adaptive quantization. The remaining
two algorithms are modified versions of the first two, in
order to obtain greater convergence speed. Finally,
performance of these four adaptive quantizers, when used
in DPCM systems, is analyzed through computer simulations.
