Comparative Study of Wavelet Transform Based Fractal Image Compression | ||
| Menoufia Journal of Electronic Engineering Research | ||
| Article 26, Volume 28, ICEEM2019-Special Issue, 2019, Pages 24-28 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/mjeer.2019.76677 | ||
| Authors | ||
| Heba Abedellatif1; Abdelrahman selim1; Taha E. Taha1; Ramadan El-Shanawany2; Osama F. Zahran1; Fathi E. Abd El-Samie1 | ||
| 1Electronics and Electrical Communications Department Faculty of Electronic Engineering Menoufia University Menouf, Egypt | ||
| 2Physics and Engineering Mathematics Department Faculty of Electronic Engineering Menoufia University Menouf, Egypt | ||
| Abstract | ||
| Fractal image compression has been studying during the last years for compressing images by using their self-similarity. The main advantages of fractal compression are, achieves a higher compression ratio and preserves the image resolution, but it lacks expensive computational cost searching the domain pool. To overcome this limitation and keeping better image quality, we propose a combination of a discrete wavelet transform and fractal coding to implement an encoder based on a flexible domain pool constructed from the neighborhoods of each range block. A comparison between recent encoding algorithms and the proposed fractal image compression introduced. | ||
| Keywords | ||
| Fractal Image Compression; Discrete Wavelet Transform (DWT); Compression Ratio | ||
| References | ||
|
[1] Y. Fisher, “Fractal Image Compression—Theory and Application”, NewYork: Springer-Verlag, 1994. [2] Frederique Cesbron, François Malassenet, “Wavelet and Fractal Transforms for Image Compression”, Sixth Conference GRETSI,France, 1997. [3] R. Rinaldo, G. Calvagno, “Image coding by block prediction of multiresolution subimages”, IEEE Transaction on Image Processing,vol. 4, no. 7, pp. 909–920, July 1995. [4] G. M. Davis, “A wavelet based analysis of fractal image compression”, IEEE Transaction on Image Processing, vol. 7, no. 2, pp. 141–154, Feb.1998. [5] Erjun Zhao Dan Liu “Fractal image compression methods: A review”, Proceedings of the Third International Conference on Information Technology and Applications (ICITA`05) 0-7695-2316-1/2005 IEEE. [6] Hochbaum, D.S. [1996]: “Approximation Algorithms for NP-Hard Problems”, PWS, Boston, 1996 [7] Brent Wohlberg and Gerhard deJager, “A review of the fractal image coding Literature”, IEEE Trans. On Image Processing, Vol.8, no.12, December 1999. [8] A. Selim, M. M. Hadhoud, M. I. Dessouky and F. E. Abd El-Samie “A simplified fractal image compression algorithm”, International Conference on Computer Engineering & System ICCES08 Ein-shams Egypt 25-27/11/2008. [9] T. Hintz and Q. Wu, “Image compression on Spiral Architecture”, Proceedings of The International Conference on Imaging Science, Systems and Technology, pp. 201-204, 2003. [10] Karami A, Yazdi M, Mercier G, “Compression of Hyper spectral ImagesUsing Discrete Wavelet Transform and Tucker Decomposition”,Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of , vol.5, no.2, pp.444,450, April 2012. [11] Gupta, R., Mehrotra, D. and Tyagi, R.K. “Comparative analysis of edge-based fractal image compression using nearest neighbor technique in various frequency domains”. Alexandria Engineering Journal (2017). | ||
|
Statistics Article View: 202 |
||
