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ON A LOSSY IMAGE COMPRESSION/RECONSTRUCTION METHOD BASED ON FUZZY RELATIONAL EQUATIONS | ||
| Iranian Journal of Fuzzy Systems | ||
| مقاله 5، دوره 1، شماره 1، تیر 2004، صفحه 33-42 اصل مقاله (327.99 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2004.492 | ||
| نویسندگان | ||
| Kaoru Hirota1؛ Hajime Nobuhara* 2؛ Kazuhiko Kawamoto2؛ Shin-ichi Yoshida2 | ||
| 1Kaoru Hirota, Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, 226-8502, Japan | ||
| 2Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, 226-8502, Japan | ||
| چکیده | ||
| The pioneer work of image compression/reconstruction based on fuzzy relational equations (ICF) and the related works are introduced. The ICF regards an original image as a fuzzy relation by embedding the brightness level into [0,1]. The compression/reconstruction of ICF correspond to the composition/solving inverse problem formulated on fuzzy relational equations. Optimizations of ICF can be consequently deduced based on fuzzy relational calculus, i.e., computation time reduction/improvement of reconstructed image quality are correspond to a fast solving method/finding an approximate solution of fuzzy relational equations, respectively. Through the experiments using test images extracted from Standard Image DataBAse (SIDBA), the effectiveness of the ICF and its optimizations are shown. | ||
| کلیدواژهها | ||
| Fuzzy relation؛ Fuzzy Relational Equation؛ Lossy Image Compression/ Reconstruction؛ Ordered Structure | ||
| مراجع | ||
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[1] A. DiNola, W. Pedrycz, and S. Sessa, Fuzzy Relational Structures: The State-of-Art, Fuzzy Sets and Systems, Vol. 75, No. 3(1995) 241-262. [2] A. DiNola, S. Sessa, W. Pedrycz, and E. Sanchez, Fuzzy Relation Equation and Their Applications to Knowledge Engineering, Kluwer Academic Publishers, 1989. [3] K. Hirota, and W. Pedrycz, Fuzzy Relational Compression, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 29 , No. 3(1999) 407-415. [4] H. Nobuhara, W. Pedrycz, and K. Hirota, Fast Solving Method of Fuzzy Relational Equation and Its Application to Lossy Image Compression/Reconstruction, IEEE Transactions on Fuzzy Systems, Vol. 8, No. 3(2000) 325-334. [5] H. Nobuhara, Y. Takama, and K. Hirota, Image Compression/Reconstruction Based on Various Types of Fuzzy Relational Equations, The Transaction of The Institute of Electrical Engineers of Japan (in Japanese), Vol. 121, No. 6 (2001) 1102-1113. [6] H. Nobuhara, Y. Takama, W. Pedrycz, and K. Hirota, Lossy Image Compression and Reconstruction Based on Fuzzy Relational Equations,Fuzzy Filters for Image Processing, Springer (2002) 339-355. [7] H. Nobuhara, W. Pedrycz, and K. Hirota, A Digital Watermarking Algorithm using Image Compression Method based on Fuzzy Relational Equation, IEEE International Conference on Fuzzy Systems, Hawaii, USA, May 12-17 (2002) (CD-Proceedings). [8] H. Nobuhara, W. Pedrycz, and K. Hirota, Fuzzy Relational Image Compression using Nonuniform Coders Designed by Overlap Level of Fuzzy Sets, International Conference on Fuzzy Systems and Knowledge Discovery (FSKD’02), 2002, Singapore (CD-Proceedings). [9] H. Nobuhara, and K. Hirota, Non-uniform Coders Design for Motion Compression Method by Fuzzy Relational Equation, International Fuzzy System AssociationWorld Congress, Istanbul, Turkey, June 29 - July 2, Lecture Notes in Artificiall Intelligence, No. 2715(2003) 428-435. [10] W. Pedrycz, Fuzzy Relational Equations with Generalized Connectives and Their Applications, Fuzzy Sets and Systems, Vol. 10 (1983) 185-201. | ||
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