1.Get a grey level image which size is N*N. (For example, 256*256, however, N = ), and partition to 8*8 sub images. 2.. Apply DCT to these sub images, and get the transformed image D with DCT coefficients for elements. 3. From D, keep the coefficient values for only upper left triangular region and set zeros for lower right region to approximate the image. (That is, only half of data is used.) 4.Take Inverse DCT to get the approximated image. 2 . Get the covariance matrix of image. 3 . Calculate the corresponding eigenvectors and eigenvalues. 4 . Represent the original image with Singular Value Decomposition. 5 . Approximate the image by taking off the 4 smallest eigenvalues. (That is, only half of information is used.)

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