Fuzzy subimage classification in image sequence coding

Author

Kong, Seong-Gon ; Kosko, Bart

Author_Institution

Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA

Volume

3

fYear

1992

fDate

23-26 Mar 1992

Firstpage

517

Abstract

Fuzzy systems are used to classify subimages efficiently in adaptive hybrid transform/predictive coding of image sequences. An adaptive fuzzy system estimates fuzzy rules by clustering input-output data generated by the subimage classification method of W.-H. Chen and C.H. Smith (1977). The fuzzy rules define patches in the state space and approximate an unknown function by covering its graph with patches. The fuzzy system classifies subimages into four temporally active subimage classes according to the between-frame prediction error signal. The system encodes active subimages with more bits, and inactive subimages with fewer bits, to compress the image data. Fuzzy classification improved coding performance over nonfuzzy classification and nonadaptive interframe coding

Keywords

fuzzy set theory; image coding; image sequences; state-space methods; active subimages; adaptive fuzzy system; adaptive hybrid transform/predictive coding; between-frame prediction error signal; graph; image sequence coding; inactive subimages; interframe coding; patches; product-space clustering; state space; subimage classification; Adaptive systems; Fuzzy sets; Fuzzy systems; Image coding; Image processing; Image sequences; Predictive coding; Signal processing; State-space methods; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on

Conference_Location

San Francisco, CA

ISSN

1520-6149

Print_ISBN

0-7803-0532-9

Type

conf

DOI

10.1109/ICASSP.1992.226162

Filename

226162