International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Contact Us

IJSTR >> Volume 10 - Issue 10, October 2021 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Wavelet Transforms And Image Approximation Based Image Compression System

[Full Text]



Taiwo Samuel Aina, Oluwaseun Olanrewaju Akinte, Babatunde Ademola, Iyaomolere, Innocent Iriaghuan Abode



Image compression, wavelet transform, Image approximation, wavelet coefficient, Discrete Wavelet Transform.



The purpose of this work is to design an efficient image compression system using wavelet transforms and image approximation by modifying the wavelet coefficient. The efficiency of the system will be tested using a test image and determining the Mean Square Error (MSE). 2D-daubechies wavelet transformation with global threshold for wavelet coefficients and numerical presentation utilizing Matlab programming are the techniques used. The Discrete Wavelet Transform (DWT) has a basic principle of splitting signals into two parts namely; the high frequencies and low frequencies. For a number of repetitions, the low frequency section is further divided into high and low frequency parts, which are generally chosen by the application. The performance of an image compression system is commonly measured by calculating the MSE and the Peak Signal to Noise Ratio (PSNR).



[1] A. Al-hamid, A. Yahya and R. El-Khoribi, "Optimized Image Compression Techniques for the Embedded Processors", International Journalof Hybrid Information Technology, vol. 9, no. 1, pp. 319-328, 2016.
[2] S. Sharma and U. Bhat, "Image Compression using an efficient hybrid algorithm", Journal on Today's Ideas-Tomorrow's Technologies, vol. 1, no. 1, pp. 45-50, 2013.
[3] P. B and S. M P, "Comparative Analysis of JPEG2000 Image Compression with Other Image Compression Standards using Discrete Wavelet Transforms Technique", International Journal of Engineering Trends and Technology, vol. 13, no. 3, pp. 107-110, 2014.
[4] N. Taujuddin and N. Lockman, " Image compression using wavelet algorithm ", Mathemat Ics, pp. 1–8. 2011.
[5] Y. Lai and C. Kuo, "A Haar Wavelet Approach to Compressed Image Quality Measurement", Journal of Visual Communication and Image Representation, vol. 11, no. 1, pp. 17-40, 2000.
[6]A. Hussain, G. AL-Khafaji and M. Siddeq, "Developed JPEG Algorithm Applied in Image Compression", IOP Conference Series: Materials Science and Engineering, vol. 928, p. 032006, 2020.
[7]S. Shelke, S. Sinha and G. Patel, "Development of complete image processing system including image filtering, image compression & image security", Materials Today: Proceedings, 2021.
[8] M. N. Do and M. Vetterli, “Contourlets: A directional multiresolution image representation,” in Proc. IEEE Inter. Conf. Image Process., Sept 2002.
[9]M. Do and M.Vetterli, “The contourlet transform: an efficient directional multiresolution image representation,” IEEE Trans. Image Process., vol. 14, no. 12, pp. 2091–2106, Dec. 2005.
[10]E. J. Cande`s and D. L. Donoho, “Ridgelets: a key to higher dimensional intermittency?” in Philos. Trans. Roy. Soc. London Ser., Sept 1999, vol. 357, no. 1760, pp. 2495–2509.
[11]E. L. Pennec and S. Mallat, “Image compression with geometrical wavelets,” in Proc. IEEE Inter. Conf. Image Process., Sept 2000, vol. 1, pp. 661–664.
[12] “Sparse geometric image representations with bandelets,” IEEE Trans. Image Process., vol. 14, no. 4, pp. 423–438, Apr. 2005.
[13]W. Lee and A. Kassim, "Signal and Image Approximation Using Interval Wavelet Transform", IEEE Transactions on Image Processing, vol. 16, no. 1, pp. 46-56, 2007.