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Pyramid Coder with Nonlinear Prediction
Panu Chaichanavong Burt/Adelson pyramid coder A nonlinear prediction Aliasing effect A switching method Conclusion
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Burt/Adelson Pyramid Coder
Introduced by Burt and Adelson (1983) Gaussian Laplacian Quantized and transmitted Original image Reconstructed image
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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
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A Nonlinear Prediction (2)
Burt/Adelson coder with nonlinear prediction
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Aliasing Effect Test image
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Aliasing Effect (2) Gaussian filter Only subsampling
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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 !
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A Switching Method (2) Input1 Input2
Input1: may be an edge, use filter1 Input2: high frequency, use filter3 How can we distinguish these inputs?
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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
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A Switching Method (4) Filtered subsampled image Filter used Filter1
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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
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A Switching Method (6) Images in the Gaussian pyramid Filter used
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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
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