Multimedia Tools and Applications

, Volume 75, Issue 23, pp 15525–15536 | Cite as

A fractal image encoding method based on statistical loss used in agricultural image compression

  • Shuai Liu
  • Zhibin ZhangEmail author
  • Lingyun Qi
  • Ming Ma


Nowadays, many images of cropland are photographed and transferred by wireless sensors in agricultural automation. But one contradiction is that the recognition needs images with high quality and the transmission needs images with small sizes. So, in this paper, by extracted and analyzedthe loss in the fractal encoding,we use fractal image encoding into the compression because of its high compression ratio. To solve the most important problemin fractal image encoding method,which is its high computational complexityand long encoding time, we first use statisticalanalysis to the fractal encoding method. We create its box-plot to find the distributional of loss value. Then, we partition them to several parts and map them to the given model. After that, we present a novel method to save the loss and maintain the quality in image compression. Finally, agricultural experimental results show effectiveness of the novel method.


Image encoding Image compression Fractal encoding Statistical loss Agricultural images 



This work is supported by Grants Programs of Higher-level talents of Inner Mongolia University [No. 125126, 115117, 135103], Scientific projects of higher school of Inner Mongolia [No. NJZY13004], Natural Science Foundation of Inner Mongolia [No.2014BS0602, 2014BS0606], National Natural Science Foundation of China [No.31160253, 31360289].

The authors wish to thank Dr. Y Ma from Universityof Surrey (UK) for his help in the grammar revision andthe anonymous reviewers for their helpful comments in reviewing this paper.

Conflict of Interest

The authors declare that there are no conflict of interest in this paper.


  1. 1.
    Ahmed F, Al-Mamun HA, Bari ASM et al (2012) Classification of crops and weeds from digital images: A support vector machine approach[J]. Crop Prot 40:98–104CrossRefGoogle Scholar
  2. 2.
    Barnsley MF, Jacquin AE (1988) Application of recurrent iterated function systems to images, in Proceedings of the SPIE. Visual Communications and Image Processing 1001:122–131Google Scholar
  3. 3.
    Bedford T, Dekking FM, Breeuwer M (1994) Fractal coding of monochrome images[J]. Signal processing: Image communication 6(5):405–419Google Scholar
  4. 4.
    Bhavani S, Thanushkodi K G. Comparison of fractal coding methods for medical image compression[J]. IET Image Processing, 7(7)(2013) 686–693Google Scholar
  5. 5.
    Binh HTT, Ngo SH (2014) All capacities modular cost survivable network design problem using genetic algorithm with completely connection encoding[J]. Human-centric Computing and Information Sciences 4(1):1–13CrossRefGoogle Scholar
  6. 6.
    Chang H T, Kuo C J. Iteration-free fractal image coding based on efficient domain pool design[J]. Image Processing, IEEE Transactions on, 9(3)(2000) 329–339.Google Scholar
  7. 7.
    DivyaUdayan JDUJ, HyungSeok Kim HSK, Jun Lee JL et al (2013) Fractal based method on hardware acceleration for natural environments[J]. Journal of Convergence 4(1):6–12Google Scholar
  8. 8.
    Fukatsu T, Kiura T, Hirafuji M (2011) A web-based sensor network system with distributed data processing approach via web application[J]. Computer Standards & Interfaces 33(6):565–573CrossRefGoogle Scholar
  9. 9.
    Goebel K, Kirk WA (1972) A fixed point theorem for asymptotically nonexpansivemappings[J]. Proc Am Math Soc 35(1):171–174MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Guo J, Kim J (2011) Adaptive Motion Vector Smoothing for Improving Side Information in Distributed Video Coding[J]. Journal of Information Processing Systems 7(1):103–110CrossRefGoogle Scholar
  11. 11.
    Yang G, Liu S, Distributed Cooperative Algorithm for Set with Negative Integer by Fractal Symmetrical Property [J]. International Journal of Distributed Sensor Networks, (2014) doi: 10.1155/2014/398583
  12. 12.
    Huffman DA (1952) A method for the construction of minimum redundancy codes[J]. Proceedings of the IRE (IEEE) 40(9):1098–1101CrossRefGoogle Scholar
  13. 13.
    Huircán JI, Muñoz C, Young H et al (2010) ZigBee-based wireless sensor network localization for cattle monitoring in grazing fields[J]. Comput Electron Agric 74(2):258–264CrossRefGoogle Scholar
  14. 14.
    Jacquin AE (1992) Image coding based on a fractal theory of iterated contractive image transformations. IEEE Trans Image Process 1(1):18–30CrossRefGoogle Scholar
  15. 15.
    Jiang G, Wang Z, Liu H (2014) Automatic detection of crop rows based on multi-ROIs[J]. Expert Systems with Applications. doi: 10.1016/j.eswa.2014.10.033 Google Scholar
  16. 16.
    K Falconer. Fractal Geometry: Mathematical Foundations and Applications, Second Edition[M].Wiley, Inc, 2003Google Scholar
  17. 17.
    Kim I K, Park R H. Still image coding based on vector quantization and fractal approximation[J]. Image Processing, IEEE Transactions on, 15(4)(1996) 587–597Google Scholar
  18. 18.
    Lai C M, Lam K M, Siu W C. A fast fractal image coding based on kick-out and zero contrast conditions[J]. Image Processing, IEEE Transactions on, 12(11)(2003) 1398–1403Google Scholar
  19. 19.
    Li J, Chen G, Chi Z (2002) A fuzzy image metric with application to fractal coding[J]. Image Processing, IEEE Transactions on 11(6):636–643CrossRefGoogle Scholar
  20. 20.
    Liu S (2014) X Cheng*, W Fu, et al. Numeric characteristics of generalized M-set with its asymptote [J]. Appl Math Comput 243:767–774MathSciNetGoogle Scholar
  21. 21.
    Liu S, Cheng X, Lan C (2013) etc., Fractal property of generalized M-set with rational number exponent [J]. Appl Math Comput 220:668–675MathSciNetzbMATHGoogle Scholar
  22. 22.
    S Liu, W Fu, H Deng, etc. Distributional Fractal Creating Algorithm in Parallel Environment [J], International Journal of Distributed Sensor Networks, (2013) doi:/ 10.1155/2013/281707
  23. 23.
    S Liu, W Fu, W Zhao, etc. A Novel Fusion Method by Static and Moving Facial Capture [J]. Mathematical Problems in Engineering, (2013) doi: 10.1155/2013/503924
  24. 24.
    Liu M, Liu S, Fu W, etc., Distributional Escape Time Algorithm based on GeneralizedFractal Sets in Cloud Environment [J]. Chinese Journal of Electronics (In press)Google Scholar
  25. 25.
    Mandelbrot BB (1982) The Fractal Geometry of Nature [M]. Freeman W H, San FranciscozbMATHGoogle Scholar
  26. 26.
    Marchant JA (1996) Tracking of row structure in three crops using image analysis[J]. Comput Electron Agric 15(2):161–179MathSciNetCrossRefGoogle Scholar
  27. 27.
    Monro D M, Dudbridge F. Fractal block coding of images[J]. Electronics letters, 28(11)(1992) 1053–1055Google Scholar
  28. 28.
    Rao K R, Yip P. Discrete cosine transform: algorithms, advantages, applications[M]. Academic press, Boston, 1990Google Scholar
  29. 29.
    Shapiro JM (1993) Embedded image coding using zerotrees of wavelet coefficients[J]. Signal Processing, IEEE Transactions on 41(12):3445–3462CrossRefzbMATHGoogle Scholar
  30. 30.
    Smart D R. Fixed point theorems[M]. CUP Archive, 1980Google Scholar
  31. 31.
    Turaga DS, Chen Y, Caviedes J (2004) No reference PSNR estimation for compressed pictures[J]. Signal Process Image Commun 19(2):173–184CrossRefGoogle Scholar
  32. 32.
    Wang X Y, Wang S G. An improved no-search fractal image coding method based on a modified gray-level transform[J]. Computers & Graphics, 32(4)(2008) 445–450Google Scholar
  33. 33.
    Zhang Y, Wang X (2012) Fractal compression coding based on wavelet transform with diamond search[J]. Nonlinear Analysis: Real World Applications 13(1):106–112MathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    Zhang L, Xiao D (2012) Collaborative image compression with error bounds in wireless sensor networks for crop monitoring[J]. Comput Electron Agric 89:1–9MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.College of Computer ScienceInner Mongolia UniversityHohhotChina
  2. 2.School of Physical Science and TechnologyInner Mongolia UniversityHohhotChina

Personalised recommendations