The lower-space vector requires less storage space and the data is thus compressed. The transformation into the subspace is usually achieved through [[projection]], or by using a [[codebook]]. In some cases, a codebook implementation can be also used to [[entropy code]] the discrete value in the same step by generating a [[prefix code]]d variable-length encoded value as its output.
The Vector Quantizationquantization, also called block quantization or pattern matching quantization, is a process of compressing K dimensional vectors to a finite set of N dimensional Vectorsvectors. <!-- The process of jointly quantizing a set of discreetdiscrete amplitude levels is called vector quantization instead of quantizing each and every sample. --> Consider a K dimensional vector <math>[x1x_1,x2x_2,...,xkx_k]</math>. TheseThis setvector (of vectors (amplitude levels) areis compressed by choosing the nearest matching vector from a set of N dimensional vectorvectors <math>[y1y_1,y2y_2,...,yny_n]</math>.
The allAll possible combinations of the N dimensional vector <math>[y1y_1,y2y_2,...,yny_n]</math> form the codebook.
Block Diagram:
A Simplesimple Vectorvector Quantizerquantizer is shown below
