CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

A New Robust Methodology of Image Compression based on CDF Wavelets

by Pradeep Kumar Pal, Kapil Dev Sharma
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 40
Year of Publication: 2019
Authors: Pradeep Kumar Pal, Kapil Dev Sharma
10.5120/ijca2019918484

Pradeep Kumar Pal, Kapil Dev Sharma . A New Robust Methodology of Image Compression based on CDF Wavelets. International Journal of Computer Applications. 182, 40 ( Feb 2019), 39-44. DOI=10.5120/ijca2019918484

@article{ 10.5120/ijca2019918484,
author = { Pradeep Kumar Pal, Kapil Dev Sharma },
title = { A New Robust Methodology of Image Compression based on CDF Wavelets },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2019 },
volume = { 182 },
number = { 40 },
month = { Feb },
year = { 2019 },
issn = { 0975-8887 },
pages = { 39-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number40/30360-2019918484/ },
doi = { 10.5120/ijca2019918484 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:13:53.955671+05:30
%A Pradeep Kumar Pal
%A Kapil Dev Sharma
%T A New Robust Methodology of Image Compression based on CDF Wavelets
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 40
%P 39-44
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

It is used specially for the compression of images where tolerable degradation is required. With the wide use of computers and consequently need for large scale storage and transmission of data, efficient ways of storing of data have become necessary. With the growth of technology and entrance into the Digital Age, the world has found itself amid a vast amount of information. Dealing with such enormous information can often present difficulties. Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages.JPEG and JPEG 2000 are two important techniques used for image compression.

References
  1. Ahmed, N., Natarajan, T., Rao, K. R., “Discrete Cosine Transform”, IEEE Trans. Computers, vol. C-23,, pp. 90-93.
  2. Wallace, G.The JPEG still picture compression standard. Communications of the ACM 34(4): 30-44.
  3. Watson, A. B. DCT quantization matrices visually optimized for individual images. Proceedings of the SPIE: 202-216 (Human Vision, Visual Processing, and Digital Display IV. Rogowitz ed. SPIE. Bellingham, WA). 1993
  4. Matthias Kramm, "Compression of Image Clusters using KarhunenLoeve Transformations", TU-M¨unchen, Institute for Computer Science, Boltzmannstr. 3, 85748 Garching, Germany.
  5. N. Ahmed, T. Natarajan, and K. R. Rao, “Discrete cosine transform,”IEEE Trans. Comput., vol. C-23, pp. 90–93.
  6. J. M. Shapiro, “Embedded image coding using zero trees of wavelet coefficients,”IEEE Trans. Signal Processing, vol. 41, pp. 3445–3462, Dec.1993.
  7. A. Said and W. A. Pearlman, “New, fast, and efficient image codec based on set partitioning in hierarchical trees,” IEEE Trans. Circuits, Syst.,Video Technol., vol. 6, pp. 243–249, June 1996.
  8. C. Chrysafis and A. Ortega, “Efficient context-based entropy coding for lossy wavelet image compression,” in Proc. 1997 Data Compression Conf., pp. 241–250 Mar. 1997
  9. J. Liang and T. D. Tran, “Fast multiplier less approximations of the DCT with the lifting scheme,” IEEE Trans. Signal Processing, vol. 49, pp.3032–3044, Dec. 2001.
  10. G. K. Wallace, “The JPEG still picture compression standard,” IEEETrans. Consumer Electronics, vol. 38, pp. 18–34, Feb. 1992.
  11. M. Atonini, M. Barlaud, P. Mathieu, and I. Daubechies, “Image coding using wavelet transform,” IEEE Trans. Image Processing, vol. 1, pp.205–220, Apr. 1992.
  12. H. S. Malvar, Signal Processing With Lapped Transforms. Norwood, A: Artech House.
  13. R. C. Gonzalea and R. E. Woods, "Digital Image Processing", 2nd Ed., PrenticeHall, 2004.
  14. Liu Chien-Chih, Hang Hsueh-Ming, "Acceleration and Implementation of JPEG2000 Encoder on TI DSP platform" Image Processing, 2007.ICIP 2007.IEEEInternational Conference on, Vo1.3, pp. III-329-339, 2005.
  15. Kumar, T. and K. Verma, 2010a. A theory based on conversion of RGB image to gray image. Int. J. Computer. Appli., 7: 5-12. DOI: 10.5120/1140-1493.
  16. .H. Yan, I. Pollak, M. N. Do, and C. A. Bouman, “Fast search for best representationsIn multi tree dictionaries,” IEEE Trans. Image Proc, vol. 15, pp. 1779–1793, July 2006.
  17. Osman G. Sezer, OztanHarmanciy, Onur G. Guleryuzy "Sparse Orthonormal Transforms For Image Compression" Georgia Institute of Technology, Atlanta, GA , USA.
  18. ChengjieTu, And Trac D. Tran, "Context-Based Entropy Coding of Block Transform" Coefficients for Image Compression, IEEE Transactions On Image Processing, VOL. 11, NO. 11 , PP-1271-1283, NOVEMBER 2002
  19. NikolayPonomarenko, Vladimir Lukin, KarenEgiazarian, JaakkoAstola. “DCT Based High Quality Image Compression”.
  20. Sugreev Kaur and Rajesh Mehra, “High Speed And Area Efficient 2d Dwt Processor Based Image Compression Signal& Image Processing: An International Journal (SIPIJ) Vol.1, No.2, December 2010.
  21. ZhigangGao, Yuan F. Zheng, “Quality Constrained Compression Using DWT Based Image Quality Metric”, Ohio State University
  22. KamrulHasanTalukder and Koichi Harada, “Discrete Wavelet Transform for Image Compression and A Model of Parallel Image Compression Scheme for Formal Verification”, , Proceedings of the World Congress on Engineering 2007 Vol IWCE 2007, July2-4, 2007
  23. SherinKishk, HosamEldin Mahmoud Ahmed and HalaHelmy,"Integral Images Compression using Discrete Wavelets and PCA", International Journal of Signal Processing, Image Processing and Pattern RecognitionVol. 4, No. 2, June, 2011.
  24. Rehna V. J, Jeya Kumar M. K, "WAVELET based image coding schemes: a recent survey", International Journal on Soft Computing (IJSC) Vol.3, No.3, August 2012.
  25. Ashanta Ranjan Routray Munesh Chandra Adhikary, "Image Compression Based on Wavelet and Quantization with Optimal Huffman Code", International Journal of Computer Applications (0975 – 8887) Volume 5– No.2, August 2010.
  26. G Boopathi, "An Image Compression Approach using Wavelet Transform and Modified Self Organizing Map", IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 2, ISSN (Online): 1694-0814, September 2011
  27. Debnath, J.K.; Rahim, N.M.S. and Wai-keung Fung, "A modified Vector Quantization based image compression technique using wavelet transform", Neural Networks,IJCNN 2008. (IEEE World Congress on Computational Intelligence) 2008, Pp: 171 – 176, 2008
  28. M. Mozammel Hoque Chowdhury and Amina Khatun, "Image Compression Using Discrete Wavelet Transform", IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 4, No 1, ISSN (Online): 1694-0814, July 2012
  29. C.SomasundarReddy, "Image Compression using Complex Wavelet Transform (CWT) with Custom Thresholding" 978-1-5090-5686-6/17 /IEEE 2017.
  30. Fahima Tabassum, Md. Imdadul Islam "A Simplified Image Compression Technique Based on Haar Wavelet Transform",978-1-4673-6676-2115/ IEEE 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Image wavelets transform compression

Powered by PhDFocusTM