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Reseach Article

Image Compression based on Quadtree and Polynomial

by Ghadah Al-khafaji
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 76 - Number 3
Year of Publication: 2013
Authors: Ghadah Al-khafaji
10.5120/13229-0658

Ghadah Al-khafaji . Image Compression based on Quadtree and Polynomial. International Journal of Computer Applications. 76, 3 ( August 2013), 31-37. DOI=10.5120/13229-0658

@article{ 10.5120/13229-0658,
author = { Ghadah Al-khafaji },
title = { Image Compression based on Quadtree and Polynomial },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 76 },
number = { 3 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 31-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume76/number3/13229-0658/ },
doi = { 10.5120/13229-0658 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:44:57.471663+05:30
%A Ghadah Al-khafaji
%T Image Compression based on Quadtree and Polynomial
%J International Journal of Computer Applications
%@ 0975-8887
%V 76
%N 3
%P 31-37
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, an efficient image compression scheme is introduced, it is based on partitioning the image into blocks of variable sizes according to its locally changing image characteristics and then using the polynomial approximation to decompose image signal with less compressed information required compared to traditional predictive coding techniques, finally Huffman coding utilized to improve compression performance rate. The test results indicate that the suggested method can lead to promising performance due to simplicity and efficiency in terms of overcoming the limitations of predictive coding and fixed block size.

References
  1. Furht, B. 1995. A Survey of Multimedia Compression Techniques and Standards. Real-Time Imaging, 1, 49-67.
  2. Singh, S. K. and Kumar, S. 2010. Mathematical Transforms and Image Compression: A Review. Maejo International Journal of Science and Technology, 4(02), 235-249.
  3. Sachin, D. 2011. A Review of Image Compression and Comparison of its Algorithms. International Journal on Electronics & Communication Technology (IJECT). 2(1), 22-26.
  4. Anitha, S. 2011. 2D Image Compression Technique-A Survey. International Journal of Scientific & Engineering Research, 2(7), 1-6.
  5. Sridevi, S. , Vijayakuymar, V. R. and Anuja, R. 2012. A Survey on Various Compression Methods for Medical Images. International Journal of Intelligent Systems and Applications, 3, 13-19.
  6. Vrindavanam, J. , Chandran, S. and Mahanti, G. K. 2012. A Survey of Image Compression Methods. Proceedings on International Conference and Workshop on Emerging Trends in Technology 12-17.
  7. Asolkar, P. S. , Zope, P. H. and Suralkar S. R. 2013. Review of Data Compression and Different Techniques of Data Compression. International Journal of Engineering Research & Technology (IJERT), 2(1), 1-8.
  8. Amruta, S. G. and Sanjay L. N. 2013. A Review on Lossy to Lossless Image Coding. International Journal of Computer Applications (IJCA), 67(17), 9-16.
  9. Fisher, Y. 1994. Fractal Image Compression: Theory and Application. Springier Verlage, New York.
  10. Vaisey, D. and Gersho, A. 1987. Variable Block-Size Image Coding. . Proceedings of the IEEE international conference on Acoustics, Speech, and Signal Processing, 1051 – 1054.
  11. Wu, P. and Zheng, B. 1998. A New Image Compression Method Based on HV Fractal and DCT. Communication Technology Proceedings, International Conference on ICCT '98. 1, 1-4.
  12. Guorui, J. , Yuzhuo, Z. , Shiqiang, Y. and Bo, Y. 1999. Fast Fractal Image Compression Based on HV Partition. Part of the SPIE Conference on Multimedia Storage and Archiving Systems. 3846, 473-481.
  13. Jamila, H. S. 2001. Fractal Image Compression , Ph. D. Thesis, College of Science, University of Baghdad.
  14. Ghada, K. T. 2001. Adaptive Fractal Image Compression. M. Sc. Thesis, National Computer Center/Higher Education Institute of Computer and Information.
  15. Golchin, F. and Paliwal, K. K. 2003. Quadtree-based classification in subband image coding. Digital Signal Processing, 13, 656–668.
  16. Rajkumar, W. S. , Kulkarni, M. V. , Dhore, M. L. , Mali, S. N. 2006. Fractal Image Compression Performance Synthesis Through HV Partitioning. Advanced Computing and Communications, ADCOM International Conference on 636 – 637.
  17. Ghada, K. T. and Luay, K. A. 2007. Merge Operation Effect On Image Compression Using Fractal Technique. Journal of Baghdad for Science, 4, 169-173.
  18. Keissarian1, F. 2009. A New Quadtree-based Image Compression Technique using Pattern Matching Algorithm. International Conference on Computational & Experimental Engineering and Sciences (ICCES), 12(4), 137-143.
  19. Chang, C-L. , Makar, M. , Sam S. T. and Girod, B. 2010. Direction-Adaptive Partitioned Block Transform for Color Image Coding. IEEE Transactions on Image Processing, 19(7), 1740-1755.
  20. George, L. E. and Sultan, B. 2011. Image Compression Based on Wavelet, Polynomial and Quadtree. Journal of Applied Computer Science & Mathematics, 11(5), 15-20
  21. Ghadah, Al-K. and George, L. E. . 2013. Fast Lossless Compression of Medical Images based on Polynomial. International Journal of Computer Applications, 70(15),28-32.
  22. Maragos, P. A. , Schafer, R. W. and Mersereau, R. M. 1984. Two-Dimensional Linear Predictive and Its Application to Adaptive Coding of Images. Proceedings of the IEEE international conference on Acoustics, Speech and Signal Processing, 1213-1229.
  23. Musmann, H. G. , Pirsch, P. and Grallert, H. 1985. Advances in Picture Coding. Proceedings of the IEEE, 73(4), 523-548.
  24. Das, M. and Loh, N. K. 1990. New Studies on Adaptive Coding of Images using Multiplicative Autoregressive Models. 10th IEEE Region Conference on Communication, 442-446.
  25. Burgett, S. and Das, M. 1993. Predictive Image Coding using Multiresolution Multiplicative Autoregressive Models. Proceedings of the IEEE, 140(2), 127-134.
  26. Balram, N. and Moura, J. M. F. 1996. Noncausal Predictive Image Codec. IEEE Transactions on Image Processing, 5(8), 1229-1242.
  27. Su, C. K. , Hsin, H. C. and Lin, S. F. 2005. Wavelet Tree Classification and Hybrid Coding for Image Compression. IEE Proceedings on Vision, Image and Signal Processing, 152(6), 752–756
  28. Iano, Y. , Silva, da. And Cruz, F. S. 2006. A Fast and Efficient Hybrid FractalWavelet Image Coder. IEEE Transactions on Image Processing, 15(1), 98–105.
  29. Xu, J. , Wu, F. and Zhang, W. 2009. Intra-Predictive Transforms for Block-Based Image Coding. IEEE Transactions on Signal Processing, 57(8), 3030- 3040.
  30. Gray, R. M. 2010. A Survey of Linear Predictive Coding: Part I of Linear Predictive Coding and the Internet Protocol. Foundations and Trends in Signal Processing, 3(3), 153-202.
  31. Rehna, V. J. and Kumar, M. K. J. 2011. Hybrid Approaches to Image Coding: A Review. International Journal of Advanced Computer Science and Applications (IJACSA), 2(7), 108-115.
  32. Groach, M. and Garg, A. 2012. Image Compression Algorithm. International Journal of Engineering Research and Applications (IJERA), 2(2), 560-567.
Index Terms

Computer Science
Information Sciences

Keywords

Image compression compression techniques quadtree and polynomial representation