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Hardware Architecture for Fractal Image Encoder with Quadtree Partitioning

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IJCA Proceedings on International Conference on Emerging Trends in Informatics and Communication
© 2016 by IJCA Journal
ICETIC 2016 - Number 1
Year of Publication: 2016
Authors:
Mamata Panigrahy
Indrajit Chakrabarti
Anindya Sundar Dhar

Mamata Panigrahy, Indrajit Chakrabarti and Anindya Sundar Dhar. Article: Hardware Architecture for Fractal Image Encoder with Quadtree Partitioning. IJCA Proceedings on International Conference on Emerging Trends in Informatics and Communication ICETIC 2016(1):23-27, September 2016. Full text available. BibTeX

@article{key:article,
	author = {Mamata Panigrahy and Indrajit Chakrabarti and Anindya Sundar Dhar},
	title = {Article: Hardware Architecture for Fractal Image Encoder with Quadtree Partitioning},
	journal = {IJCA Proceedings on International Conference on Emerging Trends in Informatics and Communication},
	year = {2016},
	volume = {ICETIC 2016},
	number = {1},
	pages = {23-27},
	month = {September},
	note = {Full text available}
}

Abstract

This paper presents the hardware architecture for fractal image compression (FIC) with quadtree partitioning. Fractal image coding with quadtree partitioning allows one to produce higher quality of image. Processing image areas of different complexity with image blocks of varying size enables proper exploration of image details. Additionally, exploiting parallelism present within the algorithm and adopting hardware based solutions speed up the encoding process. The proposed architecture has been implemented on Xilinx Vertex-5 FPGA operating at a frequency of 154MHz.

References

  • A. E. Jacquin, "Fractal image coding: a review", Proc. IEEE Vol. 81, no. 10, Oct'1993, pp. 1451–1465.
  • Y. Fisher, Fractal Image Compression: Theory and Application, Springer, New York, (1994.
  • C. Z. Tong and M. Wong, "Adaptive Approximate Nearest Neighbor Search for Fractal Image Compression", IEEE Trans. Image processing, Vol. 11, no. 6, June 2002, pp. 605-615.
  • T. K. Truong, C. M. Kung, J. H. Jeng and M. L. Hsieh, "Fast fractal image compression using spatial correlation", Chaos, Solitons and Fractals 22,2004, pp. 1071–1076.
  • S. Furao and O. Hasegawa, "A fast no search fractal image coding method", Signal Processing: Image Communication, 19, 2004, pp. 393–404.
  • B. Bani-Eqbal, "Speeding up fractal image compression", Proc. SPIE: Still-Image Compression 2418, 1995, pp. 67–74.
  • B. Wohlberg and G. D. Jager, "A review of the fractal image coding literature", IEEE Trans. Image Process. Vol. 8, no. 12, Dec 1999, pp. 1716-1729.
  • K. Belloulata and J. Konrad, "Fractal image compression with region based functionality", IEEE Trans. Image processing, Vol. 11, no. 4, April 2002. pp. 351-362.
  • D. Vidya, R. Parthasarathy, T. C. Bina and N. G. Swaroopa, "Architecture for fractal image compression", J. Syst. Arch. 46, 2000, pp. 1275–1291.
  • K P. Acken, M. J. Irwin and R. M. Owens, "A Parallel ASIC Architecture for Efficient Fractal Image Coding", Journal of VLSI Signal Processing 19, 1998, pp. 97–11
  • D. Jackson, H. Ren, X. Wu and K. G. Ricks," A hardware architecture for real-time image compression using a search-less fractal image coding method". J Real-Time Image Proc. 1, 2007, pp. 225–237.
  • M. Panigrahy, I. Chakrabarti, and A. Dhar, "VLSI design of fast fractal image encoder," in VLSI Design and Test, 18th International Symposium on, July 2014, pp. 1–2.
  • S. Samavi, M. Habibi, S. Shirani, and N. Rowshanbin, "Real time fractal image coder based on characteristic vector matching," Image Vision Computing. , vol. 28, no. 11, Nov. 2010, pp. 1557-1568.
  • M. Panigrahy, I. Chakrabarti, and A. Dhar, "Low- delay parallel architecture for fractal image compression," Circuits, Systems, and Signal Processing, (CSSP), Vol. 35, no. 3, March 2016, pp. 897-917.