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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
Article

Abstract

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.

Keywords

Image encoding Image compression Fractal encoding Statistical loss Agricultural images 

Notes

Acknowledgments

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.

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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

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