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Intensity Based Adaptive Fuzzy Image Coding Method: IBAFC

  • Deepak Gambhir
  • Navin Rajpal
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)

Abstract

A new design method of image compression as Intensity Based Adaptive Fuzzy Coding (IBAFC) is presented. In this design, the image is decomposed to non overlapping square blocks and hence each block is classified as either edge or smooth blocks. This classification based upon some predefined Threshold compared to adaptive quantization level of each block. Then each block is coded as either fuzzy F-transform compressed for edge block or mean value of block is sent for smooth block. The experimental results proves that the proposed IBAFC scheme is superior to conventional AQC and Intensity based AQC (IBAQC) on measures like MSE PSNR alongwith visual quality.

Keywords

Image Coding Fuzzy Logic Quantization 

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References

  1. 1.
    El-Khamy, et al.: A fuzzy gradient-adaptive lossy predictive coding technique. In: Proceedings of the IEEE Twentieth National Radio Science Conference, NRSC, pp. C–5 1–C–5 11 (2003)Google Scholar
  2. 2.
    Martino, F.D., et al.: Image coding/decoding method based on direct and inverse fuzzy transforms. Elsevier International Journal of Approximate Reasoning 48, 110–131 (2008)CrossRefMATHGoogle Scholar
  3. 3.
    Jeung, Y.C., Wang, J., Min, K.Y., Chong, J.W.: Improved btc using luminance bitmap for color image compression. In: Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP 2009 (2009)Google Scholar
  4. 4.
    Guo, J.M.: Improved block truncation coding using modified error diffusion. Electronics Letters 44, 462–464 (2008)CrossRefGoogle Scholar
  5. 5.
    Rayon, P., Osslan, V., Villegas, O.V., Elias, R.P., Salazar, A.M.: Edge preserving lossy image compression with wavelets and contourlets. In: Proceedings of the IEEE Int. Conference on Electronics, Robotics and Automotive Mechanics, CERMA, vol. 01, pp. 3–8 (2006)Google Scholar
  6. 6.
    Perfilieva, I.: Fuzzy transforms. Fuzzy Sets and Systems 157(8), 993–1023 (2006)CrossRefMATHMathSciNetGoogle Scholar
  7. 7.
    Yakovlev, A., Al-Azawi, S., Boussakta, S.: Performance improvement algorithms for colour image compression using dwt and multilevel block truncation coding. In: Proceeding of IEEE Intl. Conf. CSNDSP (2010)Google Scholar
  8. 8.
    Abraham, T.M., Amarunnishad, T.M., Govindan, V.K.: Improving btc image compression using a fuzzy complement edge operator. Elsevier Signal Processing Journal 88(1), 2989–2997 (2008)MATHGoogle Scholar
  9. 9.
    Abraham, T.M., Amarunnishad, T.M., Govindan, V.K.: A fuzzy complement edge operator. In: IEEE Proceedings of the Fourteenth International Conference on Advanced Computing and Communications, Mangalore, Karnataka, India (December 2006)Google Scholar
  10. 10.
    Masaki, I., Horn, B.K.P., Desai, U.Y., Mizuki, M.M.: Edge and mean based compression. MIT Artificial Intelligence Laboratory AI (Memo No.1584), pp. 533–536 (November 1996)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.University School of ITGuru Gobind Singh Inderprastha UniversityNew DelhiIndia
  2. 2.Amity School of Engineering and TechnologyNew DelhiIndia

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