Image Compression Using Principle Component Analysis


  • amel abbas
  • abbas arab
  • Jamila harbi



lossy compression, PCA, Eigenvalue, and Eigenvector.


Principle component analysis produced reduction in dimension, therefore in our proposed method used PCA in image lossy compression and obtains the quality performance of reconstructed image. PSNR values increase when the number of PCA components is increased and CR, MSE, and other error parameters decreases when the number of components is increased.


Download data is not yet available.


Milan S.,Vaclav H., and Roger B.,”Image Processing Analysis and Machine Vision”, International student edt., Thomson Learning, USA, 2008

Jamila H., Ammar I.,” An Automatic System of human face Detection and Recognition”, MSc. Thesis, Al-Mustansiriyah Uni, College of Sci., Dept. Comp. Sci., 2006.

D. Donoho, “Compressed sensing,” IEEE Transactions on Information Theory, Vol. 52, No. 4, pp. 1289-1306, Apr. 2006.

H. H. Barret, “Foundations of Image Science”, John Wiley & Sons, New Jersey, U.K., third edition, 2004.

Alexander, and Tapani Raiko,” Practical Approaches to Principal Component Analysis in the Presence of Missing Values”, Journal of Machine Learning Research 11, 2010.

G. Golub, C. Van Loan,” Matrix computations”, Academic Press, 1991, NY.

S. Haykin, Adaptive Filter Theory, 5th Edition, Prentice Hall, 2013.

Aleš Hladnik, “Image Compression and Face Recognition: Two Image Processing Applications of Principal Component Analysis”, International Circular of Graphic Education and Research, No. 6, 2013.

Vikas D .Patil, Sachin D Ruikar,”

Image Enhancement by Wavelet with Principal Component Analysis”, I J A I T I, VOLUME 1 NUMBER 3, ISSN: 2277–1891, 2012

D.D.Muresan, T.W. Parks,” Adaptive principal components and image denoising”, in: Proceedings of the 2003 International Conference on Image Processing, 14-17 Sep., vol.1, pp. I101–I104, 2003.

M.Elad,M.Aharon, ”Image denoising viasparse and redundant representations over learn addiction arises”, IEEE Transaction on Image Processing 15(12): 3736–3745. 2006

M. Aharon, M. Elad, A.M. Bruckstein, “The K-SVD: an algorithm for designing of over complete diction arise for sparse representation”, IEEE Transaction on Signal Processing 54(11): 4311–4322. 2006

P. Yaroslavsky, “Digital Signal Processing An Introduction”, Springer, Berlin, 1985.

K. Dabov, A. Foi, V.K at kovnik, K. Egiazarian, “Image denoising by sparse 3D transform-domain collaborative filtering”, IEEE Transaction on Image Processing 16(8):2080–2095, 2007.


Key Dates





Original Article

How to Cite

amel abbas, abbas arab, and J. harbi, “Image Compression Using Principle Component Analysis”, Al-Mustansiriyah Journal of Science, vol. 29, no. 2, pp. 141–147, Nov. 2018, doi: 10.23851/mjs.v29i2.256.