Fast Fractal Image Coding Method Based on RMSE and DCT Classification

Abstract:

To solve the problem of long time consuming in the fractal encoding process, a fast fractal encoding algorithm based on RMSE (Root mean square error) and DCT (Discrete Cosine Transform) classification is proposed. During the encoding process, firstly, the image is divided into range blocks and domain blocks by quadtree partition according to RMSE, then, according to DCT coefficients of image block, three classes of image blocks are defined, which are smooth class, horizontal/vertical edge class, diagonal/sub-diagonal class. At last, every range block is limited to search the best matched block in the corresponding domain block class, and the fractal coding are recorded until the process is completed. When searching the best matched block, the nearest neighbor block will be found in the sense of RMSE in the ordered codebook, and the best matched block will be further found in the vicinity of the nearest neighbor block. The experimental results show that the proposed algorithm can efficiently reduce the search space and shorten the encoding time, while achieving the same reconstructed image quality as that of the full search method.

• Periodical: Applied Mechanics and Materials (Volumes 241-244)
• Pages: 3034-3039
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.241-244.3034
• Online since: December 2012
• Authors: Xian Qiang Lv, Song Yang, Xin Zhang, Ying Wang, Yun Feng Shi, Liu Wei
• Keywords: Classification, Discrete Cosine Transform (DCT), Ractal Image Coding, Root Mean Square (RMS)

References

[1] Barnsley M F, Demko S. Iterated function systems and the global construction of fractals[J]. Proc Roy Soc 1985, A399: 243-275.

[2] Jacquin A E. Image encoding based on a fractal theory of iterated contractive image transformations[J]. IEEE Trans Image Process,1992,1(1):18-30.

DOI: 10.1109/83.128028

[3] Wohlberg B, Jager G. A review of the fractal image encoding literature. IEEE transaction on Image Proccessing, 1999, 8(12):1716-1729.

DOI: 10.1109/83.806618

[4] M.Hassaballah,M,M.Makky,Youssef B.Mahdy.A fast fractal image compression method based entropy[J].Electronic letters on Computer Vision and Image Analysis 5(1):30-40,2005.

DOI: 10.5565/rev/elcvia.82

[5] Zhang L B,Fan S.New method for fractal image compression based on adaptive threshold value quadtree[J].Computer engineering and Design,2006,27(13):2322-2323,2337.[In Chinese]

[6] Novel quadtree-based algorithm for fast fractal image compress[J].Journal of Chinese Computer System, 2007,28(6):1465-1469.[In Chinese]

[7] Yi C J.Fast fractal image encoding based on novel quadtree partition[J].Computer&Digital Engineering, 2009, (7):151-153.[In Chinese]

[8] Yang H Y, Wang X Y, Yu Y C. Fast fractal image compression based on fuzzy C-mean clustering[J].Journal of Chinese Computer System.2009,3(3):493-497.[In Chinese]

[9] Chen z p,Ye Z L,Gao X F,Meng F. A fast method for fractal image coding based on invariant quadrature[J]. Computer Engineering and Applications.2004,33:62-66.[In Chinese]

[10] Wu Y Q,Guo J J,Sun Z Y. Fast fractal coding method based on krawtchouk moments and kernel-induced fuzzy clustering[J].Journal of Optoelectronics•Laser,2010,21(9):1412-1416.[In Chinese]

[11] Fisher Y. Fractal image compression, theory and application[M]. New York: Springer-Verlag, 1994.

[12] Hütgen B. Fast Hierachical codebook search for fractal encoding of still images[J]. Proc. EOS/SPIE Visual Comm. PACS Medical APP. Berlin, Germany,1993.

[13] Wang Z, Zhang D, Yu Y. Hybrid image encoding based on partial fractal mapping[J]. Signal Processing: Image Communication, 2000, 15(9):767-779.

DOI: 10.1016/s0923-5965(99)00018-1

[14] Wang Q,Liang D Q,Bi S.Nearest neighbour for fast fractal image encoding based on correlation information feature[J].Journal of Chinese Computer System,2011,32(6):1108-1112.[In Chinese]