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A novel fast fractal image coding algorithm based on texture feature
Author(s) -
Wang Wei,
Ren Fuji,
Suzuki Motoyuki
Publication year - 2012
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.21768
Subject(s) - fractal compression , algorithm , fractal , feature (linguistics) , fractal transform , image compression , block (permutation group theory) , coding (social sciences) , computer science , cluster analysis , image texture , pattern recognition (psychology) , computational complexity theory , mathematics , artificial intelligence , image (mathematics) , image processing , mathematical analysis , linguistics , philosophy , statistics , geometry
A novel, fast fractal image coding algorithm based on the texture feature is proposed in this paper. Fractal image coding is a very promising technique for image compression. However, it has not been widely used because of the long encoding time and high computational complexity. The most fractal image encoding time is spent in determining the approximate D‐block from a large D‐blocks library by using the global searching method. Clustering the D‐blocks library is an effective method to reduce the encoding time. First, all the D‐blocks are clustered into several parts based on the new texture feature α derived from variation function; second, for each R‐block, the approximate D‐blocks are searched for in the same part. In the search process, we import control parameter δ ; this step avoids losing the most approximate D‐block for each R‐block. Finally, the R‐blocks whose least errors are larger than the threshold given in advance are coded by the quad tree method. We have performed a simulation with MATLABR2010a to verify the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm can be over 6 times faster than the moment‐feature‐based fractal image algorithm; in addition, the proposed algorithm also improves the quality of the decoded image and increases the PSNR's average value by 2 dB. The comparisons demonstrate that this method is better than the fractal image coding algorithm based on statistical features. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.