The International Arab Journal of Information Technology (IAJIT)

..............................
..............................
..............................


Image Compression based on Iteration-Free Fractal and using Fuzzy Clustering on DCT Coefficients

In the proposed method, the encoding time is reduced by combining iteration-free fractal compression technique with fuzzy c-means clustering approach to classify the domain blocks. In iteration-free fractal image compression, the mean image is considered as domain pool for range-domain mapping that reduces the number of fractal matching. Discrete Cosine Transform (DCT) coefficient is used as a new metric for range and domain blocks comparison. Also fuzzy clustering approach reduces the search space to only a subset of domain pool. Based on Fuzzy clustering on DCT space, the domain pool is grouped into three clusters and the search is made in any one of the three clusters. The proposed method has been tested for various standard images and found that the encoding time is reduced about 42 times than the iteration-free fractal coding method with only a slight degradation in the quality of images.

 


[1] Barnsley M., Fractal Everywhere, Academic Press, 1988.

[2] Bezdek J., Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Publishers, 1981.

[3] Bezdek J., Ehrlich R., and Full W., “FCM: The Fuzzy C-Means Clustering Algorithm,” Computers and Geosciences, vol. 10, no. 2, pp. 191-203, 1984.

[4] Chang H. and Kuo C., “A Novel Non-Iterative scheme for Fractal Image Coding,” Journal of Information Science and Engineering, vol. 17, no. 3, pp. 429-443, 2001.

[5] Chang H. and Kuo C., “Iteration-Free Fractal Image Coding based on Efficient Domain Pool Design,” IEEE Transactions on Image Processing, vol. 9, no. 3, pp. 329-339, 2000.

[6] Coli M., Naccarato L., and Palazzari P., “A DCT-based Metric for Fractal Image Compression,” in Proceeding of the IEEE Nordic Signal Processing Symposium, Helsinki, pp. 467-470, 1996.

[7] Curtis K., Neil G., and Fotopoulos V., “A Hybrid Fractal/DCT Image Compression Method,” in Proceeding of the 14th International Conference on Digital Signal Processing, Trinidad, pp. 1337-1340, 2002.

[8] Duh D., Jeng J., and Chen S., “DCT based Simple Classification Scheme for Fractal Image Compression,” Image and Vision Computing, vol. 23, no. 13, pp. 1115-1121, 2005.

[9] Furao S. and Hasegawa O., “A Fast no Search Fractal Image Coding Method,” Signal Processing: Image Communication, vol. 19, no. 5, pp. 393-404, 2004.

[10] Jacquin A., “Fractal Image Coding: A Review,” in Proceeding of the IEEE, London, pp. 1451- 1465, 1993.

[11] Jacquin A., “Image Coding based on a Fractal Theory of Iterated Contractive Image Transformations,” IEEE Transactions on Image Processing, vol. 1, no. 1, pp. 18-30, 1992.

[12] Jaferzadeh K., Kiani K., and Mozaffari S., “Acceleration of FIC using Fuzzy Clustering and Discrete-Cosine-Transform-Based Metric,” IET Image Processing, vol. 6, no. 7, pp. 1024-1030, 2012.

[13] Kiani K., Jaferzadeh K., Rezaie H., and Gholami S., “A New Simple Fast DCT Coefficients-based Metric Operation For Fractal Image Compression,” in Proceeding of the IEEE Second International Conference on Computer Engineering and Applications, Semnan, pp. 51- 55. 2010. 0 100 200 300 400 500 600 700 800 900 LenaParrot sPep p ersM andrilDuneSailboat T est Images Compression Time(sec) It erat ion-Free M et hod Prop osed Fuz z y Clust ering using DCT met hod 0 5 10 15 20 25 30 35 40 45 50 LenaParrot sPep p ersM andrilDuneSailboat T est Images PSNR (dB) It erat ion-free met hod Prop osed Fuz z y Clust ering using DCT met hod Image Compression based on Iteration-Free Fractal and using Fuzzy Clustering on DCT Coefficients 463

[14] Kovács T., “A Fast Classification based method for Fractal Image Encoding,” Image and Vision Computing, vol. 26, no. 8, pp. 1129-1136, 2008.

[15] Rawat C. and Meher S., “A Hybrid Image Compression using DCT and Fractal Image Compression,” The International Arab Journal of Information Technology, vol. 10, no. 6, pp. 553- 562, 2013.

[16] Saupe D., “Lean Domain Pools for Fractal Image Compression,” in Proceeding of Electronic Imaging: Science and Technology, San Jose, pp. 150-157, 1996.

[17] Truong T., Kung C., Jeng, J., and Hsieh M., “Fast Fractal Image Compression using Spatial Correlation,” Chaos, Solutions and Fractals, vol. 22, no. 5, pp. 1071-1076, 2004.

[18] Wang X. and Wang S., “An Improved no-Search Fractal Image Coding Method based on a Modified Gray-Level Transform,” Computers and Graphics, vol. 32, no. 4, pp. 445-450, 2008.

[19] Wu X., Jackson D., and Chen H., “A Fast Fractal Image Encoding Method based on Intelligent Search of Standard Deviation,” Computers and Electrical Engineering, vol. 31, no. 6, pp. 402- 421, 2005.

[20] Zhao Y. and Yuan B., “A Hybrid Image Compression Scheme Combining Block-based Fractal Coding and DCT,” Signal Processing: Image Communication, vol. 8, no. 2, pp. 73-78, 1996.

[21] Zhou Y., Zhang C., and Zhang Z., “An Efficient Fractal Image Coding Algorithm using Unified Feature and DCT,” Chaos, Solitons and Fractals, vol. 39, no. 4, pp. 1823-1830, 2009. Sobia Mahalingam obtained M.Sc and M.Phil, Degree from Thiruvalluvar University in Computer Science. She is currently pursuing Ph.D. in Computer Applications in Anna University, Chennai. She has published papers in National and International Conference. Her area of interest includes Image Processing and Optimization Techniques. Valarmathi Lakshapalam is presently working as Associate Professor in the Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India. She received her Ph.D (Computer Science and Engineering) from Bharathiar University, Coimbatore. Her research comprises Image Processing, Optimisation Techniques, Data Mining and Network Security. She has published 80 technical papers in National and International conferences and 81 papers in International Journals. Saranya Ekabaram is currently working as Assistant Professor, Department of Computer Science and Engineering, V.S.B College of Engineering Technical Campus, Coimbatore, India. She received her B.Tech.(IT) and M.E. (CSE) from Anna University, Chennai. Her area of interest includes Image Processing and Networks.