Scalar and Vector Quantization

Quantization Problem Formulation
Input:
Output:
X – random variable
f X (x) – probability density function (pdf)
{b i } i = 0..M decision boundaries
{y i } i = 1..M reconstruction levels
Discrete processes are often approximated by
continuous distributions
E.g.: Laplacian model of pixel differences
If source is unbounded, then first/last decision
boundaries = ±∞ (they are often called “saturation” values)
6/55