A vector distribution model and an effective nearest neighbor search method for image vector quantization

Author

Guan, L. ; Kamei, Masashi

Author_Institution

NCR-Canada, Waterloo, Ont.

Volume

6

Issue

12

fYear

1997

fDate

12/1/1997 12:00:00 AM

Firstpage

1688

Lastpage

1691

Abstract

In this correspondence, a modified version of Hunt´s (1980) image model is used to interpret the distribution of image data vectors. The model suggests that the diagonal line of the coordinates system is a good approximation of the principal axis of the image data vector set. The validity of the model is supported by experiments. Following this suggestion, an effective nearest neighbor search method for vector quantization of image data is developed. The method is based on partitioning the vector space using hyperplanes which are perpendicular to the diagonal direction of the coordinate system. The validity of the method is assessed by analyzing its complexity and comparing its performance to those of existing algorithms on a number of images

Keywords

computational complexity; image coding; search problems; vector quantisation; Hunt´s image model; VQ; complexity; coordinate system; coordinates system diagonal line; effective nearest neighbor search method; hyperplanes; image data vector set; image vector quantization; performance; principal axis; vector distribution model; vector space; Algorithm design and analysis; Image analysis; Iterative methods; Nearest neighbor searches; Partitioning algorithms; Performance analysis; Pixel; Search methods; Testing; Vector quantization;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.650121

Filename

650121