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Towards a Better Compression of Self Similar Images (I): Efficient IFS Encoding of Affin Self Similar Binary Images

  • Erwin Hocevar
  • Walter G. Kropatsch

Abstract

Iterated Function Systems (IFS) seem to be used best to represent objects frequently encountered in the nature, because many of them are self similar. An IFS is a set of affine and contractive transformations. The union (so-called collage) of the subimages generated by transforming the whole image produces the image again — namely the self similar attractor of these transformations, which can be described by a binary image. For a high compressed and compact mathematical representation of the images, it would be desirable to calculate the transformations directly from the image that means to solve the inverse IFS-Problem. The approaches developed so far try to solve this problem using partitioned IFS (PIFS) which encode an image by decomposing it into blocks without taking advantage of any self similarity of subimages and without using the full range of affine transformations for the mapping of these blocks onto each other. In the solution presented here the transformations (IFS-Codes) for a single image are calculated by affinely mapping maxima of the edge of the entire image to the corresponding maxima of the edge of each of the subimages. The affine invariance of an appropriate representation of quadruples thru edge extremes permits to detect extremes of the image and in each of its subimages, which are related to each other.

Keywords

Affine Transformation Similar Image Entire Image Iterate Function System Curve Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Erwin Hocevar
    • 1
    • 2
  • Walter G. Kropatsch
    • 1
    • 2
  1. 1.Dept. for Information TechnologyResearch Centre SeibersdorfSeibersdorfAustria
  2. 2.Dept. for Pattern Recognition and Image Processing, Institute of Automation 183/2Technical University of ViennaViennaAustria

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