Abstract
In this paper, a compression system with high synthetic architect is introduced, it is based on wavelet transform, polynomial representation and quadtree coding. The bio-orthogonal (tap 9/7) wavelet transform is used to decompose the image signal, and 2D polynomial representation is utilized to prune the existing high scale variation of image signal. Quantization with quadtree coding are followed by shift coding are applied to compress the detail band and the residue part of approximation subband. The test results indicate that the introduced system is simple and fast and it leads to better compression gain in comparison with the case of using first order polynomial approximation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rawat, C.S.D., Meher, S.: A Hybrid Coding Scheme Combining SPIHT and SOFM Based Vector Quantization for Effectal Image Compression. European Journal on Scientific Research 38(3), 425–440 (2009)
Loganathan, R., Kumaraswamy, Y.S.: Medical Image Compression Using Biorthogonal Spline Wavelet with Deferent Decomposition. IJCSE International Journal on Computer Science and Engineering 2(9), 3003–3006 (2010)
Dhouib, D., NaÏt-Ali, A., Olivier, C., Naceur, M.S.: Performance Evaluation of Wavelet Based Color on Brain MRI Volumetric Medical Dataset for Storage and Wireless Transmission. IJBLS 3(3), 147 (2007)
Kharate, G.K., Patil, V.H.: Color Image Compression Based On Wavelet Paket Best Tree. IJCSI International Journal on Computer Science Issues 7(2), 31–35 (2010)
Chen, Y., Hao, P.: Integer Reversable Transformation to Make JPEG Lossless. In: ICSP 2004 Proceedings, Center for Information Science, Peking University Beijing, 100871, China Department of Computer Science (2004)
Sullivan, G.J., Baker, R.L.: Efficient Quadtree Coding of Images and Vidio. IEEE Trans. Image Proce. 3(3), 327–331 (1994)
Pavlidis, T.P.: Algorithm for Graphic and Image Processing. Springer, Heidelberg (1982)
Scharinger, J.: Image Compression by Multilevel Polynomial Interpolation and Wavelet Texture Coding. In: Moreno-Díaz, R., Pichler, F. (eds.) EUROCAST 1997. LNCS, vol. 1333, pp. 429–443. Springer, Heidelberg (1997)
EL-Kuhamy, S.E., Hadhoud, M.M., Dssouky, M.I., Salam, B.M., Abd EL-Samie, F.E.: A New Approach For Adaptive Polynomial Dased Image Interpolation. International Journal of Information Aequisition 3(2), 139–159 (2006)
Muthaian, R., NeelaKantan, K., Sharma, V., Arora, A.: Image Compression and Reconstruction Using Cubic Spline Interpolation Technique. American Journal of Applied Sciences 5(11), 1562–1565 (2008)
Krishmamoorthi, R., Kannan, N.: A New Integer Image Coding Technique Based on Orthogonal Polynomials. Image and Vision Computer Archive 27(8), 999–1006 (2006)
Goerge, L.E., Sultan, B.A.: Image Compression Bsed On Wavelet,Polynomial and Quadtree. Accepted for publication in issue No. 11 of the Journal of Applied Computer Science & Mathematics (October 2011) The journal is planned to appear in early, jacs.usv.ro
Ramakrishnan, B., Sriraam, N.: Optimal Wavelet Decomposition for Wavelet Based Image Compression Coders: An Empirical Study on Medical Images, Dept. of Biological Eng., Maniple Institute of Technology, India, rama_bala@ieee.org (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Goerge, L.E., Sultan, B.A. (2011). The Use of Biorthogonal Wavelet, 2D Polynomial and Quadtree to Compress Color Images. In: Kim, Th., Adeli, H., Ramos, C., Kang, BH. (eds) Signal Processing, Image Processing and Pattern Recognition. SIP 2011. Communications in Computer and Information Science, vol 260. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27183-0_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-27183-0_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27182-3
Online ISBN: 978-3-642-27183-0
eBook Packages: Computer ScienceComputer Science (R0)