The YIQ Model of Computed Tomography Color Image Variable Block with Fractal Image Coding

전산화단층촬영 칼라영상의 YIQ모델을 가변블록 이용한 프랙탈 영상 부호화

Park, Jae-Hong;Park, Cheol-Woo

  • Received : 2016.05.18
  • Accepted : 2016.06.30
  • Published : 2016.06.30


This paper suggests techniques to enhance coding time which is a problem in traditional fractal compression and to improve fidelity of reconstructed images by determining fractal coefficient through adaptive selection of block approximation formula. First, to reduce coding time, we construct a linear list of domain blocks of which characteristics is given by their luminance and variance and then we control block searching time according to the first permissible threshold value. Next, when employing three-level block partition, if a range block of minimum partition level cannot find a domain block which has a satisfying approximation error, There applied to 24-bpp color image compression and image techniques. The result did not occur a loss in the image quality of the image when using the encoding method, such as almost to the color in the YIQ image compression rate and image quality, such as RGB images and showed good.


Fractal;Variable block;PSNR;RGB;YIQ


  1. Benoit.b.Mandelbrot, The Fractal Geometry of Nature, W.H Freeman and Company,New York, 1977.
  2. A.E.Jacquin, "Image coding based on a fractal theory of iterated contractive image transformations", IEEE Trans. Image Process.,vol.IP-1,pp.18-30,Jan.1992.
  3. D.M. Monro and F.Dudbridge, "Fractal approximation of image blocks" in Proc. Int.Conf.Acoust. Speech, Signal Processing '92. vol.3, pp.485-488, San Francisco, California, Mar. 1992.
  4. S.Lepsoy, G.E. Oien, and A. Ramstad, "Attracts image compression with a fast non-iterative decoding algorithm" in Proc.Int,Conf.Acoust.Speech.Signal Processing '93,vol.5, pp.337-340, Minneapolis, Minnesota, Apr. 1993.
  5. M.Barnsley, Fractals Everywhere, San Diego:Academeic Press, 1988.
  6. M.F Barnsley,V.Ervin,D.Hardin and J.Lancaster,"Solution of an inverse problem for fractals and other Sets", Proceedings of the National Academy of Science U.S.A, Vol.83,pp.1975-1977, 1985.
  7. A.Jacquin, A Fractal Theory of Iterated Markov Operators with Application to Digital Image Coding. PhD thesis, Georgia Institute of Technology August 1989.
  8. A.J.Crilly,R.A.Earnshaw,H.Jones, Fractals and chaos, Springer-verlag, New York,1991.
  9. M.G Alkhansari and T.S.husang,"A fractal-based image-coding algorithm", in Proc.Int, Cont. Acoust.,Speech, Signal Processing '93,vol.5,pp.345-348, Minneapolis, Minnesota ,Apr.1993.
  10. Y.Fisher, E,W.Jacobs ,R.D.Boss,"Fractal Image Compression Using Iterated Transforms", Technical Report, Naval Ocean Systems Center, San Diego, CA92142-5000.
  11. H.O.Peitgen,H.Jrgens, and D.Saupe.,Chaos and Fractals, Springer-Verlag, New York,1992.
  12. Jae-Hong Park, Cheol-Woo Park, Won-Seok Yang, "Fractal image coding for improve the quality of medical images", J. Korean. Social Radiology, Vol. 8, No. 1, 2014.
  13. Jae-Hong Park, Cheol-Woo Park, "Color image coding for variable block of fractal", J. Korean. Social Radiology, Vol. 8, No. 7, 2014.


Supported by : 춘해보건대학교