Project Name Iris image compression using Singular Value Decomposition (SVD)
A.-W.F. Hussein, Ayat Hameed · 2014
It is well known that the images often used in variety of computer applications it is difficult to store and transmit. One possible solution to overcome this problem is to use a data compression technique where an image is viewed as a matrix and then the operations are performed on the matrix. Iris image compression are achieved by using Singular Value Decomposition (SVD) technique and Principal Component Analysis (PCA) on the image matrix. The advantage of using the SVD and PCA the property of energy compaction and its ability to adapt to the local statistical variations of an image. Further, the SVD and PCA methods can be performed on any arbitrary , square, reversible and non reversible matrix of m x n size.