International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 5, No. 1, 8 Jan. 2013
Cover page and Table of Contents: PDF (size: 481KB)
A Wavelet Based Approach for Compression of Color Images
Full Text (PDF, 481KB), PP.28-35
Views: 0 Downloads: 0
Author(s)
Sarita Kumari
1,*
Index Terms
Wavelets, Color image compression, Energy retained, Entropy minimization and redundancy reduction
Abstract
The use of color in image analysis and compression is becoming more and more popular. The high quality color images are in demand, but the bandwidth and power resources are limited, this shows the requirement of effective color image compression algorithm which is suitable to human visual system. However most of the existing algorithms are designed for gray scale visual information. In this work a unique wavelet based approach is proposed for compression of color images. Wavelet families are used to characterize the quality of image by calculating quality estimation parameters, which are, peak signal to noise ratio, energy retained, entropy and redundancy. The entropy calculations are done using color histogram and coding programme is developed for estimation of PSNR, ER and redundancy of the compressed image. The results are analyzed and a set of criteria is determined for the acceptability of coding algorithm. Results show that Biorthogonal wavelet filter outperforms the orthogonal one in quality of compressed image but the orthogonal filter is more energy preserving.
Cite This Paper
Sarita Kumari, "A Wavelet Based Approach for Compression of Color Images", International Journal of Modern Education and Computer Science (IJMECS), vol.5, no.1, pp.28-35, 2013. DOI:10.5815/ijmecs.2013.01.04
Reference
[1]H. Nobuhara and K. Hirota, “Color Image Compression/Reconstruction by YUV Fuzzy Wavelets”, IEEE Annual Meeting of the Fuzzy Information, 2, pp 774 – 779, 2004.
[2]S. Annadurai, and M. Sundaresan, “Wavelet Based Enhanced Color Image Compression Relying on Sub-Band Vector Quantization”, ICGST-GVIP Journal, 9 (1), pp 9-16, 2009.
[3]S. Kumari, V. S. Meel, R. Vijay, “Image Quality Prediction by Minimum Entropy Calculation for Various Filter Banks”, International Journal of Computer Applications, 7(5), pp 31-34, Nov. 2007.
[4]S. Kumari and R. Vijay, "Image Quality Estimation by Entropy and Redundancy Calculation for Various Wavelet Families," Journal of International Academy of Physical Sciences, 15(1), pp 1-8, 2011.
[5]M. K. Mandal, “Choice of Wavelets for Image Compression”, Lecture Notes in Computer Science, pp 1133, pp 239-249, 1995.
[6]T. N. Kanvel and E. C. Monie, “Performance Measure of Different Wavelets for a Shuffled Image Compression Scheme”, International Journal of Computer Science and Network Security, 9(3), pp 215-221, March, 2009.
[7]Y. K. Jain, “Performance Analysis and Comparison of Wavelet Families Using for Image Compression”, International Journal of Soft Computing, 2(1), pp 161-171, 2007.
[8]Subhasis Saha & Rao Vemuri “Analysis Based Adaptive Wavelet Filter Selection in Lossy Image Coding Schemes”, ISCAS-2000-IEEE International Symposium on Circuits and Systems, Geneva, Switzerland, May, 2000.
[9]Bryan E. Usevitch “A Tutorial on Modern Lossy Wavelet Image Compression: Foundations of JPEG 2000” IEEE Signal Processing Magazine, September 2001.
[10]John D. Villasenar, Benjamin Belzer and Judy Liao, “Wavelet Filter Evaluation for Image Compression”, IEEE Transactions on Image Processing, 4(8), 1995.
[11]Satyabrata Rout “Orthogonal Vs Biorthogonal Wavelets for Image Compression”, MS Thesis, Virgina Polytechnic Institute and State University, Virgina, August, 2003.
[12]Yinfen Low and Rosli Besar “Wavelet based Medical Image Compression using EZW”, Proceedings of 4th National Confreence on Telecommunication Technology, Shah Alam, Malaysia, pp 203-206, 2005.
[13]Michael B. Martin "Application of Wavelets to Image Compression" M.S. Thesis, Blacksburg Virigina, 1999.
[14]A. Gentile, and F. Sorbello, “Image Processing Chain For Digital Still Cameras Based on the Simple Architecture”, Proceedings of the 2005 International Conference on Parallel Processing Workshops, Washington DC, USA: IEEE Computer Society, pp 215–222, 2005.
[15]T. Alpcan, M. Kesal, and H. Deliç, “Wavelet Based Subband Vector Quantization Algorithm for Gray Images,” Proceedings of the IASTED International Conference on Signal And Image Processing, Las Vegas, Nevada, October 28-31, pp720-724, 1998.