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

  • Authors:

  • H. Nobuhara;W. Pedrycz;K. Hirota

  • Affiliations:

  • Department of Computational Intelligence and Systems Science Tokyo, Institute of Technology, 4259 Nagatsuta, 226-8502, Midiri-ku Yokohama, Japan;Department of Electrical and Computer Engineering, University of Alberta, 4259 Nagatsuta, T6R 2G7, Edmonton, Canada;Department of Computational Intelligence and Systems Science Tokyo, Institute of Technology, 4259 Nagatsuta, 226-8502, Midiri-ku Yokohama, Japan

  • Venue:
  • Soft Computing - A Fusion of Foundations, Methodologies and Applications
  • Year:
  • 2005

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Abstract

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.