CLASSICAL AREAS OF PHENOMENOLOGY |
Prev
Next
|
|
|
An improved fast fractal image compression using spatial texture correlation |
Wang Xing-Yuan(王兴元)†, Wang Yuan-Xing(王远星), and Yun Jiao-Jiao(云娇娇) |
Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China |
|
|
Abstract This paper utilizes a spatial texture correlation and the intelligent classification algorithm (ICA) search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm 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 a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same.
|
Received: 02 March 2011
Revised: 06 May 2011
Accepted manuscript online:
|
PACS:
|
42.30.Va
|
(Image forming and processing)
|
|
42.30.Wb
|
(Image reconstruction; tomography)
|
|
87.57.C-
|
(Image quality)
|
|
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 60573172 and 60973152), the Superior University Doctor Subject Special Scientific Research Foundation of China (Grant No. 20070141014), and the Natural Science Foundation of Liaoning Province of China (Grant No. 20082165). |
Cite this article:
Wang Xing-Yuan(王兴元), Wang Yuan-Xing(王远星), and Yun Jiao-Jiao(云娇娇) An improved fast fractal image compression using spatial texture correlation 2011 Chin. Phys. B 20 104202
|
[1] |
Barnsley M F and Demko S 1985 Math. Phys. Sci. 399 243
|
[2] |
Barnsley M F 1988 Fractal Everywhere (New York: Academic) p. 34
|
[3] |
Barnsley M F, Elton J H and Hardin D P 1989 Constr. Approx. 5 3
|
[4] |
Jacquin A E 1992 IEEE T. Image Process. 1 18
|
[5] |
Barthel K U and Voye T J 1994 Proc. Int. Workshop Image Process. Budapest, Hungary
|
[6] |
Davoine F and Chassery J M 1994 12th Int. Conference Pattern Recogn. (Los Alamitos: IEEE Computer Society Press) 1 801
|
[7] |
Davoine F, Svensson J and Chassery J M 1995 Proc. Int. Conference Image Process. Washington D.C. 3 284
|
[8] |
Davoine F, Antonini M, Chassery J M and Barlaud M 1996 IEEE T. Image Process. 5 338
|
[9] |
Fisher Y 1994 Fractal Image Compression, Theory and Application (New York: Springer-Verlag)
|
[10] |
Saupe D and Jacob S 1997 Electron. Lett. 33 46
|
[11] |
Fisher Y 1995 Fractal Image Compression: Theory and Applications (Berlin: Springer-Verlag) p. 121
|
[12] |
Furao S and Hasegawa O 2004 Signal Process. Image 19 393
|
[13] |
Kominek J 1995 Proc. SPIE 2419 296
|
[14] |
Truong T K, Kung C M, Jeng J H and Hsieh M L 2004 Chaos, Solitons and Fractals 22 1071
|
[15] |
Yoo H W, Jang D S, Jung S H, Park J H and Song K S 2002 Pattern Recogn. 35 749
|
[16] |
Ma W Y and Manjunath B S 1996 Proc. IEEE Int. Conference Comput. Vision Pattern Recog. Boston, USA p. 425
|
[17] |
Manjunath B S and Ma W Y 1996 IEEE TPAMI 18 837
|
[18] |
Tamura H, Mori S and Yamawaki T 1978 IEEE Trans. Syst. Man, Cybern. 8 460
|
[19] |
Mao J and Jain A K 1992 Pattern Recogn. 25 173
|
[20] |
Haralick R M, Shanmugam K and Dinstein I 1973 IEEE Trans. Syst. Man, Cybern. 3 610
|
[21] |
Hermes T, Klauck C, Krey W J and Zhang J 1995 Proc. SPIE 2420 394
|
[22] |
Sakamoto H, Suzuki H and Uemori A 1994 Proc. SPIE 2185 25
|
[23] |
Rui Y, Huang T S and Mehrotra S 1998 Relevance Feedback Techniques in Interactive Content-Based Image Retrieval (Proc. SPIE) 3312 25
|
[24] |
Tsai D M and Lin B T 2002 J. Mater. Eng. Performance 20 420
|
[25] |
Xiao B, Li J and Gao X B 2009 Acta Electron. Sin. 37 2205 (in Chinese)
|
[26] |
Torre L D, Vrscay E R, Ebrahimi M and Barnsley M F 2009 SIAM J. Imaging Sci. 2 470
|
[27] |
Wu X W, Jackson D J and Chen H C 2005 Comput. Electron. Eng. 31 402
|
[28] |
Furao S and Hasegawa O 2004 Signal Process. Image 19 393
|
[29] |
Kovacs T 2008 Image Vision Comput. 26 1129
|
[30] |
Pang C Y, Zhou Z W and Guo G C 2006 Chin. Phys. 15 3039
|
[31] |
Pang C Y, Zhou Z W, Chen P X and Guo G C 2006 Chin. Phys. 15 618
|
[32] |
Long G L and Xiao L 2004 Phys. Rev. A 69 052303
|
[33] |
Ren H P, Ping Z L, Bo W R G, Sheng Y L, Chen S Z and Wu W K 2003 Chin. Phys. 12 610
|
[34] |
Atef M, William P and Mohamed S B 2010 J. Electron. Imaging 19 1
|
[35] |
Duan L L, Liao X F and Xiang T 2010 Commun. Nonlinear Sci. Numer. Simul. Accepted
|
[36] |
Xu Y, Dong J T and Wang S H 2010 Acta Phys. Sin. 59 7535 (in Chinese)
|
No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|