Package smile.vq

Vector quantization is a lossy compression technique used in speech and image coding.

See: Description

Interface Summary

Interface	Description
Neighborhood	The neighborhood function for 2-dimensional lattice topology (e.g.
VectorQuantizer	Vector quantizer with competitive learning.

Class Summary

Class	Description
BIRCH	Balanced Iterative Reducing and Clustering using Hierarchies.
GrowingNeuralGas	Growing Neural Gas.
NeuralGas	Neural Gas soft competitive learning algorithm.
NeuralMap	NeuralMap is an efficient competitive learning algorithm inspired by growing neural gas and BIRCH.
SOM	Self-Organizing Map.

Package smile.vq Description

Vector quantization is a lossy compression technique used in speech and image coding. In vector quantization, a vector is selected from a finite list of possible vectors to represent an input vector of samples. Each input vector can be viewed as a point in an n-dimensional space. The vector quantizer is defined by a partition of this space into a set of non-overlapping regions. The vector is encoded by the nearest reference vector (known as codevector) in the codebook.