1. Pyramid Coder with Nonlinear Prediction
Panu Chaichanavong
Burt/Adelson pyramid coder
A nonlinear prediction
Aliasing effect
A switching method
Conclusion

2. Burt/Adelson Pyramid Coder
Introduced by Burt and Adelson (1983)
Gaussian
Laplacian
Quantized
and transmitted
Original image
Reconstructed image

3. A Nonlinear Prediction
By Florencio and Schafer (1994)
Filter Just subsample!
Interpolator
Replication
Weighted median of 6 neighbor pixels
Median of 4 neighbor pixels 1
Lower resolution image
Predicted image

4. A Nonlinear Prediction (2)
Burt/Adelson coder
with nonlinear prediction

5. Aliasing Effect
Test image

6. Aliasing Effect (2)
Gaussian filter
Only subsampling

7. Linear low-pass with weight shown above
A Switching Method
Let’s look at the following 4x4 filters
Filter3
Linear low-pass with weight shown above
Filter2
Median of average
Filter1
Pick one !

8. A Switching Method (2) Input1 Input2
Input1: may be an edge, use filter1
Input2: high frequency, use filter3
How can we distinguish these inputs?

9. A Switching Method (3)
Let D = sum of difference of adjacent pixel values
sd = standard deviation of 16 pixel values
p = D/sd
Decision criterion:
p < 18, use filter1
18 < p < 22, use filter2
p > 22, use filter3

10. A Switching Method (4) Filtered subsampled image Filter used Filter1

11. A Switching Method (5) Reconstructed image Method PSNR (dB)
Bit-rate (bit/pixel)
B/A
32.77
0.8278
Nonlinear
34.00
0.6544
Switch
33.58
0.6501
Reconstructed image

12. A Switching Method (6) Images in the Gaussian pyramid Filter used

13. Conclusion
Low-pass filter reduces aliasing effect but gives blurred image
Some nonlinear prediction preserves edges and details but may introduces annoying aliasing
A decision criterion is presented to switch among various filters to select an appropriate one for a particular input