Scalar and Vector Quantization
Scalar and Vector Quantization
Scalar and Vector Quantization
You also want an ePaper? Increase the reach of your titles
YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.
Jayant Quantizer<br />
N. S. Jayant showed in 1973 that ∆ adjustment based<br />
on few observations still works fine:<br />
<br />
<br />
If current input falls in the outer levels, exp<strong>and</strong> step size<br />
If current input falls in the inner levels, contract step size<br />
The total product of expansions <strong>and</strong> contraction should be 1<br />
Each decision interval k has a multiplier M k<br />
If input s n–1 falls in the k th interval, step size is multiplied by M k<br />
Inner-level M k < 1, outer-level M k > 1<br />
<br />
Step size adaptation rule:<br />
∆<br />
=<br />
n<br />
M l ( n−1)<br />
n−<br />
where l(n–1) is the quantization interval at time n–1.<br />
∆<br />
1,<br />
30/55