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Archetype Classification in an Iterated Transformation Image Compression Algorithm

In: Fractal Image Compression

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  • R. D. Boss
  • E. W. Jacobs

Abstract

Determining a good set of transformations that encode an image well is time consuming because for each range an extensive search through the candidate domains is required ([47]). The purpose of classification is to reduce the number of domains that have to be checked in order to find an acceptable covering. References [11] and [44] use a classification scheme based on the idea that by orienting blocks in a canonical form (based on brightness), and then subdividing these primary classes further by the location of strong edges, it should be possible to find good coverings with minimal computation. In fact, this type of classification method performs quite well. In this chapter, a different classification method using archetypes is presented (see [11]). The method for generating a set of archetypes is described, and the archetypes are then used to classify ranges and domains in an iterated transformation image compression encoder. Fidelity versus encoding time data are presented, and compared with a more conventional classification scheme.

Suggested Citation

  • R. D. Boss & E. W. Jacobs, 1995. "Archetype Classification in an Iterated Transformation Image Compression Algorithm," Springer Books, in: Yuval Fisher (ed.), Fractal Image Compression, chapter 0, pages 79-90, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4612-2472-3_4
    DOI: 10.1007/978-1-4612-2472-3_4
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