Description: 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.)
- [dwprotected] - Matlab source code for DCT-based waterma
- [hidedctadv] - Information hiding algorithm based on DC
File list (Check if you may need any files):
hw2zhengyan.m
Download