Relational image compression: optimizations through the design of fuzzy coders and YUV color space

For Image Compression and reconstruction method based on Fuzzy relational equations (ICF), two optimizations are proposed. First optimization is to propose an invariant index for the design of appropriate coders in ICF, we call an overlap level of fuzzy sets. Second optimization corresponds to application of YUV color space to the existing ICF. Through the experiment of image compression and reconstruction using 1000 test images extracted from Corel Gallery, the invariance of the overlap level is confirmed. Furthermore, by the experimental comparison of the proposed method (ICF over YUV color space) and the conventional one (ICF over RGB color space) using 1000 test images, it is confirmed that the Peak Signal to Noise Ratio of the proposed method is increased at a rate of 7.1%∼13.2% compared with the conventional one, under the condition that the compression rates are 0.0234∼0.0938.

[1]  Witold Pedrycz,et al.  On Measures of Fuzziness of Solutions of Fuzzy Relation Equations with Generalized Connectives , 1985 .

[2]  Witold Pedrycz,et al.  A decomposition of fuzzy relations , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[3]  Raghu Krishnapuram,et al.  A robust approach to image enhancement based on fuzzy logic , 1997, IEEE Trans. Image Process..

[4]  Witold Pedrycz,et al.  A digital watermarking algorithm using image compression method based on fuzzy relational equation , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[5]  Elie Sanchez,et al.  Resolution of Composite Fuzzy Relation Equations , 1976, Inf. Control..

[6]  Joan L. Mitchell,et al.  JPEG: Still Image Data Compression Standard , 1992 .

[7]  Witold Pedrycz.  Optimization schemes for decomposition of fuzzy relations , 1998, Fuzzy Sets Syst..

[8]  W. Pedrycz.  Fuzzy relational equations with generalized connectives and their applications , 1983 .

[9]  Witold Pedrycz,et al.  Fast solving method of fuzzy relational equation and its application to lossy image compression/reconstruction , 2000, IEEE Trans. Fuzzy Syst..

[10]  Yasufumi Takama,et al.  Image Compression/Reconstruction Based on Various Types of Fuzzy Relational Equations , 2001 .

[11]  W. Pedrycz.  Processing in relational structures: fuzzy relational equations , 1991 .

[12]  Isabelle Bloch,et al.  Fuzzy mathematical morphologies: A comparative study , 1995, Pattern Recognit..

[13]  W. Pedrycz,et al.  DECOMPOSITION PROBLEM OF FUZZY RELATIONS , 1985 .

[14]  Isabelle Bloch,et al.  Fuzzy Relative Position Between Objects in Image Processing: A Morphological Approach , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Witold Pedrycz,et al.  Fuzzy relational compression , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[16]  Kaoru Arakawa,et al.  Fuzzy rule-based signal processing and its application to image restoration , 1994, IEEE J. Sel. Areas Commun..

[17]  Kaoru Arakawa,et al.  Median filter based on fuzzy rules and its application to image restoration , 1996, Fuzzy Sets Syst..

[18]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..