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Fractal image compression using upper bound on scaling parameter

Author

Listed:
  • Roy, Swalpa Kumar
  • Kumar, Siddharth
  • Chanda, Bhabatosh
  • Chaudhuri, Bidyut B.
  • Banerjee, Soumitro

Abstract

This paper presents a novel approach to calculate the affine parameters of fractal encoding, in order to reduce its computational complexity. A simple but efficient approximation of the scaling parameter is derived which satisfies all properties necessary to achieve convergence. It allows us to substitute to the costly process of matrix multiplication with a simple division of two numbers. We have also proposed a modified horizontal-vertical (HV) block partitioning scheme, and some new ways to improve the encoding time and decoded quality, over their conventional counterparts. Experiments on standard images show that our approach yields performance similar to the state-of-the-art fractal based image compression methods, in much less time.

Suggested Citation

  • Roy, Swalpa Kumar & Kumar, Siddharth & Chanda, Bhabatosh & Chaudhuri, Bidyut B. & Banerjee, Soumitro, 2018. "Fractal image compression using upper bound on scaling parameter," Chaos, Solitons & Fractals, Elsevier, vol. 106(C), pages 16-22.
    • Handle: RePEc:eee:chsofr:v:106:y:2018:i:c:p:16-22
    DOI: 10.1016/j.chaos.2017.11.013
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