vector quantization signal processing (2 resultados)

paperback. Condición: New. Language:Chinese.Pages Number: 306 Publisher: Science Pub. Date :2011-07-01 version 1. Vector quantization in multimedia signal processing applications (author Luzhe Ming. Zheng Weimin. SUN Sheng) is the vector quantization theory and application of monographs. The book first introduces the basic principles of vector quantization and application status. and then introduce the structure of vector quantization and basic ideas. and then describes the basic vector quantization of the three key te.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - High Quality Content by WIKIPEDIA articles! Vector quantization is a classical quantization technique from signal processing which allows the modeling of probability density functions by the distribution of prototype vectors. It was originally used for data compression. It works by dividing a large set of points (vectors) into groups having approximately the same number of points closest to them. Each group is represented by its centroid point, as in k-means and some other clustering algorithms. The density matching property of vector quantization is powerful, especially for identifying the density of large and high-dimensioned data. Since data points are represented by the index of their closest centroid, commonly occurring data have low error, and rare data high error. This is why VQ is suitable for lossy data compression. It can also be used for lossy data correction and density estimation. Vector quantization is based on the competitive learning paradigm, so it is closely related to the self-organizing map model.