A Proposed Lossy Image Compression based on Multiplication Table

Mohammed Salih Mahdi; Nidaa Falih Hassan

doi:10.24017/science.2017.3.34

Authors

Mohammed Salih Mahdi BIT Dept, Business Information College, University of Information Technology and Communications, Baghdad, Iraq
Nidaa Falih Hassan Computer science Dept, University of technology, Baghdad, Iraq

Abstract

Lately, Internet improved in the various trends, especially, the use of the image increased due to the daily use in several scopes like social media (Facebook, Twitter, WhatsApp, etc.), connected devices (sensor, IP camera, Internet of Things (IoT) Internet of Everything (IoE), etc) and smart phone devices that users interchanged images estimated in the billions. So, images issues in internet can be summarized into two criteria, the first criteria is considered with transmit image size. The second criteria is considered with low bandwidth through transmission. This paper exhibits a methodology for image compression using an idea of multiplication Table. The suggested algorithm helpful in realizing a preferable achievement by presenting a high Compression Ratio, preserve image quality with a high PSNR, small losing in the original image and efficiently in running time.

Keywords:

lossy compression, multiplication Table, IoT

References

[1] S. Kaur, V. Sulochana, "Design and Implementation of LZW Data Compression Algorithm", International Journal of Information Sciences and Techniques (IJIST) Vol.2, No.4, July 2012.
https://doi.org/10.5121/ijist.2012.2407
[2] K. Sayood," Introduction to Data Compression", Third Edition, University of Nebraska, Lincoln, Morgan Kaufmann is an imprint of Elsevier,2000.
[3] M. Nelson, L. Gailly, "The data compression" 2nd Edition. M&T Books New York. 1996.
[4] M. Pu, "Fundamental Data Compression",1st Edition, Butterworth-Heinemann. 2005.
https://doi.org/10.1016/B978-075066310-6/50004-0
[5] M. Pu, "Fundamental Data Compression", Butterworth-Heinemann is an imprint of Elsevier Linacre House, Jordan Hill, Oxford OX2, Corporate Drive, 2006.
[6] D. Salomon, M. Giovanni," Handbook of Data Compression", Fifth Edition, Springer London Dordrecht Heidelberg New York, British Library Cataloguing in Publication Data Springer-Verlag London, 2010.
[7] J. Santoso, L. Nugroho, G. Suparta, R. Hidayat, " Compression Ratio and Peak Signal to Noise Ratio in Grayscale Image Compression using Wavelet", International Journal of Computer Science and Technology 2 (2): 7-11, 2011.
[8] O. Khalifa, "Wavelet Coding Design for Image Data Compression", the international Arab Journal of Information Technology 2 (2): 118-127, 2005.
[9] P. Dhumal, S. Deshmukh, "Survey on Comparative Analysis of Various Image Compression Algorithms with Singular Value Decomposition", International Journal of Computer Applications, Vol. 133, No. 6, PP. 18-21, 2016.
https://doi.org/10.5120/ijca2016907725
[10] Z. Abood, "Composite Techniques Based Color Image Compression", Journal of Engineering Number 3 Volume 23 March, Baghdad, 2017
[11] N. Asia , R. Heba, "Lossless Color Image Compression Based on Folding Technique", The Forth Scientific Conference of the College of Science University of Kerbala, Journal of University of Kerbala, 2016.