A fast fractal image encoding method based on intelligent search of standard deviation

  • Authors:

  • Xianwei Wu;David Jeff Jackson;Hui-Chuan Chen

  • Affiliations:

  • Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487-0286, USA;Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487-0286, USA;Department of Computer Science, The University of Alabama, Tuscaloosa, AL 35487-0290, USA

  • Venue:
  • Computers and Electrical Engineering
  • Year:
  • 2005

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Abstract

In this paper we present a fast fractal encoding method based on an intelligent search of a Standard Deviation (STD) value between range and domain blocks. First, we describe the basic fractal image compression theory and an improved bit allocation scheme for Jacquin's Iterated Function System (IFS) parameter. Experimental results show that using a Fixed Scale Parameter (FSP) can shorten encoding time without significantly affecting reconstructed image quality. Second, we present a search algorithm based on the STD introduced by Tong. We enhance Tong's STD search algorithm by introducing a domain Intelligent Classification Algorithm (ICA) based on STD-classified domain blocks. The domain block search pool is pruned by eliminating multiple domain blocks with similar STD values. We refer to this pruning as the De-Redundancy Method (DRM). The domain search process is adaptive with the range block STD value of interest controlling the size of the domain pool searched. We refer to this process as the Search Number Adaptive Control (SNAC). Finally, we present experimental results showing the efficiency of the proposed method, noting a significant improvement over Tong's original STD method without significant loss in the reconstructed image quality.