Monday 19th of February 2018

An Adaptive Fractal Image Compression

Taha Mohammed Hasan and Xinagqian Wu

In this paper an Adaptive Fractal Image Compression (AFIC) algorithm is proposed to reduce the long time of the Fractal Image Compression (FIC). AFIC worked on; minimizing the complexity and the number of matching operations by reducing both of the range and domain blocks needed in the matching process, for this purpose Zero Mean Intensity Level Fractal Image Compression based on Quadtree partitioning, Variance Factor Range Exclusion, Variance Factor Domain Selection and Domain Pool Reduction techniques is used. This in turn will affect the encoding time, compression ratio and the image quality. The results show that AFIC significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image. In comparison with some resent methods, the proposed method spends much less encoding time, get higher compression ratio while the quality of the reconstructed images is almost the same.

Keywords: Fractal, range block, quadtree, variance, image compression, encoding time

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Taha Mohammed Hasan
received his BSc, MSc in computer science from Mansour University College and The University of Mustansiriyah, Baghdad, Iraq in1992 and 2006 respectively. He is currently pursuing the Ph.D. degree at the Harbin Institute of Technology (HIT), Harbin, China. His research interests is the image processing.

Xinagqian Wu
Received his B.Sc., M.Sc. and Ph.D. in computer science from Harbin Institute of Technology (HIT), Harbin, China in 1997, 1999 and 2004, respectively. Now he is a professor in the School of Computer Science and Technology, HIT. His current research interests include pattern recognition, image analysis and biometrics, etc.

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