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

Authors: Shimal Das, Dibyendu Ghoshal

Abstract:

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.

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

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1110924

References:

[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