A Proposed Hybrid Color Image Compression Based on Fractal Coding with Quadtree and Discrete Cosine Transform

Fractal based digital image compression is a specific
technique in the field of color image. The method is best suited for
irregular shape of image like snow bobs, clouds, flame of fire; tree
leaves images, depending on the fact that parts of an image often
resemble with other parts of the same image. This technique has
drawn much attention in recent years because of very high
compression ratio that can be achieved. Hybrid scheme incorporating
fractal compression and speedup techniques have achieved high
compression ratio compared to pure fractal compression. Fractal
image compression is a lossy compression method in which selfsimilarity
nature of an image is used. This technique provides high
compression ratio, less encoding time and fart decoding process. In
this paper, fractal compression with quad tree and DCT is proposed
to compress the color image. The proposed hybrid schemes require
four phases to compress the color image. First: the image is
segmented and Discrete Cosine Transform is applied to each block of
the segmented image. Second: the block values are scanned in a
zigzag manner to prevent zero co-efficient. Third: the resulting image
is partitioned as fractals by quadtree approach. Fourth: the image is
compressed using Run length encoding technique.

• Shimal Das
• Dibyendu Ghoshal

• Fractal coding
• Discrete Cosine Transform
• Iterated Function System (IFS)
• Affine Transformation
• Run length encoding.

[1] Palanisamy G. and Samukutti A., “Medical Image Compression using a
Novel Embedded Set Partitioning Significant and Zero Block Coding,”
International Arab Journal of Information Technology, vol. 5, no. 2, pp.
132-139, 2008.
[2] Radhakrishnan S. and Subramaniam J., “Novel Image Compression
using Multi-Wavelets with SPECK Algorithm,” International Arab
Journal of Information Technology, vol. 5, no. 1, pp. 45- 51, 2008.
[3] M.F. Barnsley, Fractal everywhere (M), New York: Academic Press,
1988.
[4] A.E. Jaquin, Image coding based on a fractal theory of iterated
contractive image transformations (J), IEEE Transactions on Image
Processing, 1(1): 18-30, 1992.
[5] Y.Fisher, Fractal Image compression (J), Fractals, 2(3): 321-329, 1994.
[6] H.L. Zhao, Z. Liang, N. Y .Soma, Fractal color image compression(C),
XIII Brizilian Symposium on Computer Graphics and Image Processing
(SIBGRAPI’00), 185-192, 2000.
[7] Kulkarni S., Naik A., and Nagori N., “A Comparison of Real Valued
Transforms for Image Compression,” International Journal of
Engineering and Natural Sciences, vol. 4, no. 1, pp. 17, 2008.
[8] Kesavan H., “Choosing a DCT Quantization Matrix for JPEG
Encoding,” available at: http://scien.stanford.edu/pages/labsite/1997/
ee39 2c/demos/kesavan
[9] Annadurai S. and Sundaresan M., “Wavelet Based Color Image
Compression using Vector Quantization and Morphology,” in
Proceedings of the International Conference on Advances in Computing,
Communication and Control, USA, pp. 391-396, 2009.
[10] Krikor L., Baba S., Arif T., and Shaaban Z., “Image Encryption using
DCT and Stream Cipher,” European Journal of Scientific Research, vol.
32, no. 1, pp. 48-58, 2009.
[11] Selim, A., Hadhoud, M. and Salem, M.O.,” A Comparison Study
between Spiral and Traditional Fractal Image Compression,”
International Conference on Computer Engineering & Systems, pp.39 –
44, 2002.
[12] Gaganpreet Kaur, Hitashi, Gurudev Singh,” Performance Evaluation of
Image Quality Based on Fractal Image Compression,” International
Journal of Computers & Technology, Volume 2 No.1, February 2012.
[13] Sofia Douda, Abdallah Bagri, Amer Abdelhakim El Imrani, ”A reduced
domain pool based on DCT for a fast fractal image encoding”,
Electronic Letters on Computer Vision and Image Analysis, 10(1):11-
23, 2011
[14] Ruhiat Sultana, Nisar Ahmed and Shaik Mahaboob Basha, “Advanced
Fractal Image Coding Based on the Quadtree,” Computer Engineering
and Intelligent Systems, Vol 2, No.3, ISSN 2222-1719 (Paper) ISSN
2222-2863 (Online).
[15] Gohar Vahdati, Habib Khodadadi, Mahdi Yaghoobi and Mohammad-R.
Akbarzadeh-T, “Fractal Image Compression Based on Spatial
Correlation and Hybrid Particle Swarm Optimization with Genetic
Algorithm,” 22nd International Conference on Software Technology and
Engineering (ICSTE), 2010.
[16] Kharate G. and Patil V., “Color Image Compression Based on Wavelet
Packet Best Tree,” International Journal of Computer Science Issues,
vol. 7, no. 2, pp. 31- 35, 2010.
[17] D. Venkatasekhar and P. Aruna, “A Fast Fractal Image Compression
Using Huffman Coding,” Asian Journal of Computer Science and
Information Technology, vol. 2, no. 9 pp. 272- 275, 2012. [18] Khalil M., “Image Compression using New Entropy Coder,”
International Journal of Computer Theory and Engineering, vol. 2, no. 1,
pp. 39-42, 2010.
[19] Vijaya-Prakash A. and Gurumurthy K., “A Novel VLSI Architecture for
Digital Image Compression using Discrete Cosine Transform and
Quantization,” International Journal of Computer Science and Network
Security, vol. 10, no. 9, pp. 175-182, 2010.
[20] Yih-Lon Lin and Wen-Lin Chen, “Fast Search Strategies for Fractal
Image Compression,” Journal of Information Science and Engineering,
28, 17-30, 2012.
[21] Shouji Chen, Liming Zhang: Fractal and image compression. Shanghai
Science And Technology Education Publishing House(1998)
[22] A. P. Pentland, “Fractal-based descriptions of natural scenes,” IEEE
Trans. Pattern Anal. Machine Intell, vol. PAMI-6, no. 6, 1984.
[23] M. F. Barnsley and A. D. Sloan, “A better way to compress images,”
BYTE, pp. 215–223, January 1988.
[24] Chandan Singh Rawat and Sukadev Meher, “A Hybrid Image
Compression Scheme using DCT and Fractal Image Compression,” The
International Arab Journal of Information Technology, Vol. 10, No. 6,
November 2013.
[25] Jessica.F. (2002),”Algorithms for Massive Data Sets Context-based
Compression,” CS 493,1:1- 3,www.docstoc.com/docs/54164044/Runlength
encoding