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On-board Implementation of Fractal Compression of Satellite Images using Distributed Networked GPUs

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 69 - Number 14
Year of Publication: 2013
Munesh Singh Chauhan
Sharmi S
Abeer Marhoon Al-sideiri

Munesh Singh Chauhan, Sharmi S and Abeer Marhoon Al-sideiri. Article: On-board Implementation of Fractal Compression of Satellite Images using Distributed Networked GPUs. International Journal of Computer Applications 69(14):17-20, May 2013. Full text available. BibTeX

	author = {Munesh Singh Chauhan and Sharmi S and Abeer Marhoon Al-sideiri},
	title = {Article: On-board Implementation of Fractal Compression of Satellite Images using Distributed Networked GPUs},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {69},
	number = {14},
	pages = {17-20},
	month = {May},
	note = {Full text available}


On-board image compression has been a growing trend in most recent satellite missions. Since majority of satellite applications deal with imagery; compression of images due to limited on-board data storage mediums has become a necessity. The idea of treating satellite imageries as fractals and then encoding them provides an efficient way of conserving bandwidth and per-bit storage costs. Fractal encoding is characterized by slow encoding times which somehow had hindered its popularity in spite of its impressive compression ratio scaling many orders as compared to JPEG. In order to circumvent this handicap, Fractal compression is implemented using powerful GPUs (Graphical Processor Units) that are capable of reaching astronomical computing speeds of around 900 GFLOPS (Quadro Graphic Cards from NvidiaTM) with internal memory bandwidth ranging to 100 GB/s. This astounding parallel capability is probed to be used on board systems, providing much needed boost for image compression. As the decoding part of compressed fractal images is almost instantaneous, this part can be handled without any specific hardware at the ground station level. Further, the issue of on-board data storage mechanisms is discussed with emphasis on use of HDD instead of SSD and flash memories. In sum, the prime aim is to provide a seamless image compression mechanism coupled with de-compression at ground station level thus providing real-time streaming of satellite images from satellite to the ground.


  • Zhou G. , Kafatos M. , Future Intelligent Earth Observing Satellites, Proc. SPIE 5151, Earth Observing Systems VIII, 1 (November 13, 2003)
  • Si X. , Zheng H. , High Performance Remote Sensing Image Processing Using CUDA, 3rd International Symposium on Electronic Commerce and Security, 29-31 July, 2010
  • Yu G. , Vladimirova T. , Sweeting M. N. , Image Compression Systems On Board Satellites, http://dx. doi. org/10. 1016/j. actaastro. 2008. 12. 006
  • Fisher Y. , Bielefeld B. , Lawrence A. , Greenwood D. , NETROLOGIC Inc. , Fractal Image Compression, Contract # N00014-91-C-0117, Quaterly Progress Report 8-12-9
  • Antonio Lopes F. , Roberto d'Amore, A low complexity Image Compression Solution for Onboard Space Applications, SBCCI '10 ACM Proceedings of the 23rd symposium on Integrated circuits and system design
  • Guoxia Yu, Tanya Vladimirova, Martin Sweeting, An Efficient On-Board Lossless Compression Design for Remote Sensing Image Data, Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
  • C. Lambert-Nebout, C. Latry, G. A. Moury, C. Parisot, M. Antonini, M. Barlaud, On-board optical image compression for future high-resolution remote sensing systems, in: Proceedings of SPIE on Applications of Digital Image Processing XXIII, vol. 4115, December2000, pp. 332–346.
  • G. Yu, T. Vladimirova, X. Wu, M. N. Sweeting, A new automatic on-board multispectral image compression system for Leo Earth observation satellites, in: The 15th IEEE International Conference on Digital Signal Processing, 2007, pp. 395-398
  • Kiely A. , Klimesh M. , The ICER Progressive Wavelet Image Compressor, IPN Progress Report 42-155, November 15, 2003
  • NVIDIA, CUDA C Programming Guide, PG-02829-001_v5. 0, October 2012, Design Guide