Acknowledgement
We would like to express our high regard to our families for their encouragement and inspiration supported us, and without which, we would not have come this far. Many thanks go to supervisor Dr. Hanan and our deep appreciation for continuous guidance and her prompt help and provide advice support to helped us finalize our project and offered deep insight into the study. Also, special thanks should be given to group friends that worked on this project for the kindness, cooperation, positive energy, constant motivational words and caring throughout the whole project.
References
- Gouet-Brunet V. (2009) Image Representation. In: LIU L., OZSU M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_1438
- Marques O. (2008) Image Data Representations. In: Furht B. (eds) Encyclopedia of Multimedia. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-78414-4_343
- Claudia Jeffrey, Raster vs Vector Graphics - Ultimate Guide, May 21, 2020, accessed March 2021.
- Applying Color Theory to Digital Media and Visualization, Rhyne, Theresa-Marie, Boca Raton, FL: CRC press, 2016. 184 pp. ISBN 9781498765497.
- Image Pixels, http://shutha.org/node/789 accessed in March 2021.
- Nag, S. (2017). Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression. ArXiv, abs/1710.05311.
- Adokar, D. U., & Gurjar, A. A. (2020). Image Compression using Vector Quantization. Grenze International Journal of Engineering & Technology (GIJET), 6(2), p. 69.
- Paek, J., & Ko, J. (2017). $K$-Means Clustering-Based Data Compression Scheme for Wireless Imaging Sensor Networks. IEEE Systems Journal, 11, 2652-2662. https://doi.org/10.1109/JSYST.2015.2491359
- Ammah, P.N., & Owusu, E. (2019). Robust medical image compression based on wavelet transform and vector quantization. Informatics in Medicine Unlocked, 15, 100183. https://doi.org/10.1016/j.imu.2019.100183
- K. Mounika, D. Sri Navya Lakshmi, K. Alekya and M.R.N. Tagore, "SVD Based Image Compression", International Journal of Engineering Research and General Science, Vol. 3, No. 2, pp. 1-5, 2015.
- Wu, Y.G. and S.C. Tai, 2001. Medical image compression by discrete cosine transform spectral similarity strategy. IEEE T. Informat. Technol. Biomed., 5(3): 236-243. https://doi.org/10.1109/4233.945294
- Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," Image Processing, IEEE Transactions on, vol. 13, no. 4, pp. 600-612, 2004 https://doi.org/10.1109/TIP.2003.819861
- Z. Wang and A. Bovik, "Mean squared error: Love it or leave it? a new look at signal fidelity measures," Signal Processing Magazine, IEEE, vol. 26, no. 1, pp. 98 -117, Jan. 2009
- J. HAN AND M. KAMBER, Data mining: concepts and techniques, Morgan Kaufmann Publishers, Inc., 3rd edition 2011.
- D. Lam and D. C. Wunsch, "Clustering," Academic Press Library in Signal Processing," Signal Processing Theoryand Machine Learning, vol. 1, 2014.
- K-Means Clustering Algorithm, https://www.javatpoint.com/k-means-clustering-algorithmin-machine-learning, Accessed in March 2021.
- D. Sisodia, L. Singh,S. Sisodia,K. Saxena,"Clustering Techniques: A Brief Survey of Different Clustering Algorithms", International Journal of Latest Trends in Engineering and Technology (IJLTET), Vol.1 Issue3 September 2012.
- Arzoo, K., & Rathod, K.R. (2017). K-Means algorithm with different distance metrics in spatial data mining with uses of NetBeans IDE 8.2.
- Mr. Chandresh KParmar, Prof. Kruti Pancholi,-A Review on Image Compression TechniquesII Journal of Information, Knowledge And Research in Electrical Engineering ISSN:0975-6736 volume-02, Issue-02 Nov12 to Oct13