Vector quantization for image compression based on fuzzy clustering

Author

Boudraa, Abdel-Ouahab ; Kanafani, Qosai ; Beghdadi, Azeddine ; Zergainoh, Anissa

Author_Institution

Inst. Galilee, Univ. de Paris-Nord, Villetaneuse, France

Volume

2

fYear

1999

fDate

1999

Firstpage

835

Abstract

In this paper a codebook design for image compression based on the fuzzy c-means (FCM) algorithm is presented. The codebook design from training vectors is viewed as a fuzzy clustering problem of unlabeled data points into clusters. Due to computational cost of FCM to generate the codebook, a fast version (FFCM), which operates on the image histogram, is used to obtain a good initial codebook to start the FCM algorithm. Experimental results are presented to illustrate the performance of the proposed compression method

Keywords

fuzzy set theory; image coding; pattern clustering; vector quantisation; FCM algorithm; VQ; codebook design; fuzzy c-means algorithm; fuzzy clustering; image compression; image histogram; training vectors; unlabeled data points; vector quantization; Australia; Clustering algorithms; Costs; Data compression; Distortion measurement; Image coding; Image processing; Signal processing algorithms; Speech processing; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Its Applications, 1999. ISSPA '99. Proceedings of the Fifth International Symposium on

Conference_Location

Brisbane, Qld.

Print_ISBN

1-86435-451-8

Type

conf

DOI

10.1109/ISSPA.1999.815801

Filename

815801