What Is Vector Quantization?
Strictly speaking, quantization is the procedure of approximating continuous with discrete values; in practice, the input values to the quantization procedure are often also discrete, but with a much finer resolution than that of the output values. The goal of quantization usually is to produce a more compact representation of the data while maintaining its usefulness for a certain purpose. For example, to store color intensities you can quantize floating-point values in the range [0.0, 1.0] to integer values in the range 0-255, representing them with 8 bits, which is considered a sufficient resolution for many applications dealing with color. In this example, the spacing of possible values is the same over the entire discrete set, so we speak of uniform quantization; often, a nonuniform spacing is more appropriate when better resolution is needed over some parts of the range of values. Floating-point number representation is an example of nonuniform quantization—you have the as many p
