The week's pick
Reseach Article
Fractal Image Compression of Satellite Imageries
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 30 - Number 3 |
| Year of Publication: 2011 |
| Authors: Veenadevi.S.V, A.G.Ananth |
10.5120/3621-5056
|
Veenadevi.S.V, A.G.Ananth . Fractal Image Compression of Satellite Imageries. International Journal of Computer Applications. 30, 3 ( September 2011), 33-36. DOI=10.5120/3621-5056
Abstract
Fractal image coding has the advantage of higher compression ratio, but is a lossy compression scheme. The encoding procedure consists of dividing the image into range blocks and domain blocks and then it takes a range block and matches it with the domain block. The image is encoded by partitioning the domain block and using Affine transformation to achieve fractal compression. The image is reconstructed using iterative functions and inverse transforms. In the present work the fractal coding techniques are applied for the compression of satellite imageries. The compression ratio and Peak Signal to Noise Ratio (PSNR) values are determined for three types of images namely standard Lena image, Satellite Rural image and Satellite Urban image. The Matlab simulation results for the reconstructed image for 4 iterations show that for a compression ratio ~3.2 and PSNR values achievable for Lena image ~12, Satellite Rural image ~17.0 and for Satellite urban image ~22. Comparison of the present results with the EZW coding indicates that, the fractal compression techniques are found more effective for compression of Satellite Urban imageries since the Satellite Urban images contains more fractals compared to that of Satellite Rural image and Lena image.
References
- Arnaud E. Jacquin, “Fractal image coding”, Proceedings of IEEE VOL.81, pp. 1451-1465, 1993.
- Barnsley MF. ” Fractal everywhere”, 2nd ed, San Deigo Academic Press, 1993.
- M.F Barnsley, Alan d.Salon, “Better way to compress images’, 1993.
- Arnaud E. Jacquin A, “Image coding based on a fractal theory of iterated contractive image transformations”, IEEE Trans Image processing , 1992, pp. 18-30
- Fisher Y, editor, “Fractal image compression: theory and application”, New York, Springer-Verlag, 1995.
- Dr. Muhammad Kamran, Amna Irshad Sipra and Muhammd Nadeem, “A novel domain optimization technique in fractal image compression”, IEEE Proceedings of the 8th World Congress on Intelligent Control and Automation, 2010, pp. 994-999.
- Bohong Liu and Yung Yan, “An Improved Fractal Image Coding Based on the Quadtree”, IEEE 3rd International Congress on Image and Signal Processing, 2010,pp. 529-532.
- Kai Shuang, Ning Xiao, Feng Xu, Dayue Lv and Wang Yu, “ Fractal Compression Coding based on Fractal Dimension Feature Blocks”, IEEE International Symposium on Information Science and Engineering, 2008, pp. 223-226.
- Hui Yu, Li Li, Dan Liu, Hongyu Zhai, Xiaoming Dong, “ Based on Quadtree Fractal Image Compression Improved Algorithm for Research”, IEEE Trans, 2010, pp.1-3.
- Chelehgahi, Mehdi Masoudi, Jaferzadeh, Keyyan, Nia, Mohsen Derakhshan, “ A high speed intelligent classification algorithm for fractal image compression using DCT coefficients”, IEEE 3rd International conference on ICCSN, May 2011, pp.156-159.
- Zhuang Wu, Bixi Yan, “An effective fractal image compression algorithm” IEEE International conference on ICCASM, 2010, pp.139-143.
- Hosseini, Shookooh, Shahhosseini, Beizaee, “Speeding up fractal image de-compression”, IEEE International conference on ICCAIE, 2010, pp.521-526.
- K. Nagamani and A.G.Ananth, “Study of embedded zero tree wavelet (EZW) compression techniques for high and low resolution satellite imageries”, International Journal of Technology and Engineering System, 2010, pp. 141-145.
Index Terms
Keywords