Title :
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
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;
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
DOI :
10.1109/ISSPA.1999.815801
