Difference between revisions of "Haar wavelet (2x2)"
From ScienceZero
| Line 9: | Line 9: | ||
p2 p3 | p2 p3 | ||
| − | Forward transformation | + | Forward 2D Haar transformation |
b0 = (p0 + p1) + (p2 + p3) 'DC level | b0 = (p0 + p1) + (p2 + p3) 'DC level | ||
b1 = (p0 - p1) + (p2 - p3) 'Horizontal difference | b1 = (p0 - p1) + (p2 - p3) 'Horizontal difference | ||
| Line 15: | Line 15: | ||
b3 = (p0 - p1) - (p2 - p3) 'Horizontal and vertical difference | b3 = (p0 - p1) - (p2 - p3) 'Horizontal and vertical difference | ||
| − | Reverse transformation | + | Reverse 2D Haar transformation |
p0 = ((b0 + b1) + (b2 + b3)) / 4 | p0 = ((b0 + b1) + (b2 + b3)) / 4 | ||
p1 = ((b0 - b1) + (b2 - b3)) / 4 | p1 = ((b0 - b1) + (b2 - b3)) / 4 | ||
Revision as of 03:23, 3 September 2007
The two-dimensional Haar wavelet is useful for transforming data into 4 bands where b0 contains the DC level of each block while b1-b3 contains higher frequency components.
By recursively applying the transformation on b0 until b0 reaches a size of 1 we get frequency information about different areas in the picture at different scales. After the transformations most values will have a low value (assuming that the input data is a normal photograph), by using quantization and setting very low values to zero the data will compress very well using fairly simple compression algorithms like Huffman.
Input block of 2x2 pixels p0 p1 p2 p3
Forward 2D Haar transformation b0 = (p0 + p1) + (p2 + p3) 'DC level b1 = (p0 - p1) + (p2 - p3) 'Horizontal difference b2 = (p0 + p1) - (p2 + p3) 'Vertical difference b3 = (p0 - p1) - (p2 - p3) 'Horizontal and vertical difference
Reverse 2D Haar transformation p0 = ((b0 + b1) + (b2 + b3)) / 4 p1 = ((b0 - b1) + (b2 - b3)) / 4 p2 = ((b0 + b1) - (b2 + b3)) / 4 p3 = ((b0 - b1) - (b2 - b3)) / 4
