## Tuesday, October 30, 2007

### Basic image compression technologies

1 . Block Truncation Coding (link)
1. divide image into 4x4 or 8x8 blocks
2. Calculate the average gray level of the block
3. use a threshold to get binary block b and mu, ml (greater and less then th)
4. The encoder writes mu, ml and b to a file
5. In the decoder, an image block is reconstructed by replacing the 1's with mu and the 0's by ml.
2. Gaussian Pyramids (link): Each level in the pyramid is 1/4 of the size of previous level.
1. low-pass filter the image,
2. down-sampling image,
3. up-sampling image,
4. low-pass filter again.
3. Singular Value Decomposition (link). A = USV^T, U and V are orthogonal matrices (left/right singular vectors), and S is a diagonal matrix (contains the singular values). The size of U S V is m \times m, mxn, nxn, respectively.
This method approximated the mxn image by using far fewer entries than in original matrix. By using the rank of a matrix, we remove the redundant information. Matlab code: