We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Cosine Transformation of Image Coordinates for Reduction of Image Loss due to Compression and Decompression

by Md. Ashek-Al-Aziz, Abdullah-Hil Muntakim
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 19
Year of Publication: 2020
Authors: Md. Ashek-Al-Aziz, Abdullah-Hil Muntakim
10.5120/ijca2020920705

Md. Ashek-Al-Aziz, Abdullah-Hil Muntakim . Cosine Transformation of Image Coordinates for Reduction of Image Loss due to Compression and Decompression. International Journal of Computer Applications. 175, 19 ( Sep 2020), 10-14. DOI=10.5120/ijca2020920705

@article{ 10.5120/ijca2020920705,
author = { Md. Ashek-Al-Aziz, Abdullah-Hil Muntakim },
title = { Cosine Transformation of Image Coordinates for Reduction of Image Loss due to Compression and Decompression },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2020 },
volume = { 175 },
number = { 19 },
month = { Sep },
year = { 2020 },
issn = { 0975-8887 },
pages = { 10-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number19/31558-2020920705/ },
doi = { 10.5120/ijca2020920705 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:25:27.803211+05:30
%A Md. Ashek-Al-Aziz
%A Abdullah-Hil Muntakim
%T Cosine Transformation of Image Coordinates for Reduction of Image Loss due to Compression and Decompression
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 19
%P 10-14
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Discrete Cosine Transformation (DCT) is a popular image compression technique, but it is lossy compression because of image losses found after decompression. In order to reduce the loss, a new algorithm with model of Cosine Transformation of Image Coordinates (CTIC) has been proposed. The earlier method with Matlab standard built in function and proposed model – both have been implemented over 100 Google images. It has been found that CTIC method has much less loss than in the existing DCT method’s Matlab built in functions dct2(.) and idct2(.). Also PSNR and Energy Compaction Ratio have been calculated for both the image transformation techniques.

References
  1. Thota N. R. and Devireddy S. K., Image Compression Using Discrete Cosine Transform, Georgian Electronic Scientific Journal: Computer Science and Telecommunications, 3(17), 2008
  2. Watson A. B., Image Compression Using the Discrete Cosine Transform, Mathematica Journal, 4(1), 1994, 81-88
  3. Raid A.M., Khedr W.M., El-dosuky M. A. and Ahmed W., Jpeg Image Compression Using Discrete Cosine Transform - A Survey, International Journal of Computer Science & Engineering Survey (IJCSES), 5(2), April, 2014
  4. Alotaibi R. A., Elrefaei L. A., Text-image watermarking based on integer wavelet transform (IWT) and discrete cosine transform (DCT), Applied Computing and Informatics, 15, 2019, 191–202
  5. Shaheen A. M., Sheltami T. R., Al‑Kharoubi T. M., Shakshuki E., Digital image encryption techniques for wireless sensor networks using image transformation methods: DCT and DWT, Journal of Ambient Intelligence and Humanized Computing, Springer-Verlag GmbH Germany, 2018
  6. Elharar E., Stern A., Hadar O. and Javidi B., A Hybrid Compression Method for Integral Images Using Discrete Wavelet Transform and Discrete Cosine Transform, Journal Of Display Technology, 3(3), 2007
  7. Ahmed N., Natarajan T., and Rao K. R., Discrete Cosine Transform, IEEE Transactions on Computers, January, 1974
  8. Fracastoro G., Fosson S. M., Magli E., Steerable Discrete Cosine Transform, IEEE Transactions on Image Processing, 26(1), October 24, 2018
  9. Zeng B. and Fu J., Directional Discrete Cosine Transforms—A New Framework For Image Coding, IEEE Transactions On Circuits And Systems For Video Technology, 18(3), March, 2008
  10. Pogrebnyak O. and Lukin V. V., Wiener discrete cosine transform-based image filtering, Journal of Electronic Imaging 21(4), Oct-Dec, 2012
  11. Wu Y. G. and Tai S. C., Medical Image Compression by Discrete Cosine Transform Spectral Similarity Strategy, IEEE Transactions On Information Technology In Biomedicine, 5(3), September, 2001
  12. Lee B. G., A New Algorithm to Compute the Discrete Cosine Transform, IEEE Transactions On Acoustics, Speech, And Signal Processing, 32(6), December, 1984
  13. Chan Y. H. and Siu W. C., Mixed-Radix Discrete Cosine Transform, IEEE Transactions On Signal Processing, 41(II), November, 1993
  14. Aburdene M. F., Zheng J. and Kozick R. J., Computation of Discrete Cosine Transform Using Clenshaw’s Recurrence Formula, IEEE Signa L Processing Letters, 2(8), August, 1995
  15. Hou H. S., A fast recursive algorithm for computing the discrete cosine transform, IEEE Transactions On Acoustics, Speech, And Signal Processing, 35(10), October, 1987
  16. Strang G., The Discrete Cosine Transform, SIAM Review, 41(1), Society for Industrial and Applied Mathematics, 1999, 135–147
  17. Wang Z. and Hunt B. R., The Discrete Cosine Transform—-A New Version, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE, 1983
  18. Cariolaro G., Erseghe T. and Kraniauskas P., The Fractional Discrete Cosine Transform, IEEE Transactions On Signal Processing, 50(4), April, 2002
  19. Chitprasert B. and Rao K. R., Discrete Cosine Transform Filtering, Signal Processing, 19, 1990, Elsevier, 233-245,
  20. Obukhov and Kharlamov, Discrete Cosine Transform for 8x8 Blocks with CUDA, NVIDIA, 2008
  21. Hafed Z. M. and Levine M. D., Face Recognition Using the Discrete Cosine Transform, International Journal of Computer Vision 43(3), 2001, 167–188
  22. Bhandari A. K., Kumar A. and Padhy P. K., Enhancement of Low Contrast Satellite Images using Discrete Cosine Transform and Singular Value Decomposition, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 5(7), 2011
  23. Kumar A., Bhandari A. K., Padhy P., Improved normalised difference vegetation index method based on discrete cosine transform and singular value decomposition for satellite image processing, IET Signal Process., 6(7), 2012, 617–625
  24. Narasimha M. J. and Peterson A. M., On the Computation of the Discrete Cosine Transform, IEEE Transactions On Communications, 26(6), June, 1978
  25. Zhao Y. and Yuan B., Image compression using fractals and discrete cosine transform, Electronics Letters, 30(6), 17th March, 1994, 474-475
  26. Robinson J. and Kecman V., Combining Support Vector Machine Learning with the Discrete Cosine Transform in Image Compression, IEEE Transactions On Neural Networks, 14(4), July, 2003
  27. Wan Z., Pruning the Fast Discrete Cosine Transform, IEEE Transactions on Communications, 39(5), May, 1991
  28. Yang Y., Galatsanos N. P., Katsaggelos A. K., Regularized Reconstruction to Reduce Blocking Artifacts of Block Discrete Cosine Transform Compressed Images, IEEE Transactions On Circuits And Systems For Video Technology, 3(6), December, 1993
  29. Wang Z., A Fast Algorithm for the Discrete Sine Transform Implemented by the Fast Cosine Transform, IEEE Transactions On Acoustics, Speech, And Signal Processing, 30(5), October, 1982
  30. Mandyam G., Ahmed N. and Magotra N., Lossless Image Compression Using the Discrete Cosine Transform, Journal Of Visual Communication And Image Representation, 8(1), March, 1997, 21–26
  31. Halsall F., Multimedia Communications Applications, Networks, Protocols and Standards, Pearson Education, 1988, 116-17.
Index Terms

Computer Science
Information Sciences

Keywords

Image Compression Decompression Discrete Cosine Transformation Inverse DCT CTIC ICTIC