The growth of digital technology over the past decades is leading to new challenges like storage and transmission of digital images. As the digital image in its raw form occupies more storage space and takes longer time for transmission. Several image compression methods exist to address this issue and fractal image compression is one among the popular image compression methods. But fractal image compression has a disadvantage of more encoding time. In this paper, we have proposed a new architecture for fractal image compression. The proposed architecture is modeled using Verilog HDL, synthesized using Xilinx ISE 14.2, implemented on Xilinx Spartan 6 FPGA board and is tested on Standard Lena image[512x512]. The proposed architecture will reduce the design cycle time and the implementation cost. The results of the proposed architecture have shown a considerable reduction in encoding time to 5.897ns when compared to software implementation.

Architecture, Fractal Image Compression, FPGA, Quadtree Decomposition

[1] M. Gloria, “Trends in medical image compression”, Current Medical Imaging Reviews, pp. 1-21, 2006.
[2] S. Bhavani, K. Thanushkodi, “A novel fractal image coding for quasi-lossless medical image compression”, European Journal of Scientific Research, Vol.70 No.1, 2012.
[3] C. Frigaard, J.T. Gade, Hemmingsen., T. Sand, “Image compression based on a fractal theory”, Institute for Electronic Systems, Aalborg University, 1994.
[4] M. Barnsley, “Fractal Everywhere”, Academic Press, San Diego, 1993.
[5] J. Arnaud, “Image coding based on a fractal theory of iterated contractive image transformation”, IEEE Transaction on Image Processing, pp18-30.Vol.1, No.1, 1992.
[6] Y. Fisher, “Fractal image compression Theory and Application”, Springer Verlag, 1995.
[7] M. Panigrahy,I. Chakrabarti, A. S. Dhar, “Hardware implementation of quadtree based fractal image decoder”, IEEE Xplore , 2016.
[8] S. Padmashree, Rohini Nagapadma, “Comparative analysis of quadtree partitioned iterated function systems for medical Images,” International Journal for computer applications, 0975-8887, vol. 59, 2012.
[9] M. Bharathi, T. Janani, “Fractal Image Compression Using Quantum Search Algorithm”, Journal of Computational and Theoretical Nanoscience, 2017.
[10] J. Wang, Y. Tian, X. Meng, T. Liu, “FPGA implement method for two-dimensional integer wavelet transform in the space-based on-orbit image compression system”, Proceedings SPIE 10256, Second International Conference on Photonics and Optical Engineering, 2017.
[11] C.Y. Yuen, O.Y. Lui, K.W. Wong, “Hybrid fractal image coding with quadtree-based progressive structure,” J.Vis. Commun. Image Represent, pp. 1328–1341, 2013.
[12] X. Y. Wang, “An improved fast fractal image compression using spatial texture correlation”, Chinese Physics. B, Vol. 20, 2011.
[13] Chetan, S Deepak, “Fractal image compression using quadtree decomposition and DWT”, International Journal of Scientific Engineering and Research, 2015.
[14] A. G. Ananth, “Fractal Image Compression of Satellite Color Imageries Using Variable Size of Range Block”, International Journal of Image Processing, 2014.
[15] C. Thirumoorthi, "Edge Based Compression Using Embedded Zero Wavelet Tree (EBC –EZWT) for Lung Cancer CT Scan Medical Image", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.653-659, 2018
[16] S. D. Kasute, M. Kolhekar, "ROI Based Medical Image Compression", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.1, pp.6-11, 2017