Compound Coding

1
Compound Coding

• Mark Kalman
• Isaac Keslassy
• Daniel Wang
• 12/6/00

2
Outline

• Motivating Compound Coding
• Classification Algorithms
• Work from the Literature
• Spatial
• DCT-Based Methods
• Wavelets
• Our DCT Codebook Method
• Comparison
• Smoothing
• An Example

3
Motivating Compound Coding

• At 1bit/pel
• Median graphics PSNR 31dB
• Median text PSNR 19.5 dB
• Different properties gt different coding

4
Past Work (1)

• Spatial Domain Algorithms
• Chen (1990) block variance
• Bones et al (1990) edges (Sobel filter)
• DCT-Based Algorithms
• Chaddha et al (1995) DCT-18 Absolute-Sum
• Konstantinides (2000) DCT Bit Rate

5
Past Work (2)

• Wavelet-based Algorithms
• c2 goodness of fit to Laplacian
• similar to MSE
• discreteness of distribution
• concentration of data in peaks

6
Our DCT Codebook Scheme

• DCT coefficient values are differently
  distributed in text and graphic regions
• Each distribution is given by a codebook histogram

graphics
text
Distribution for DCT coefficient (2,1)
7
The Algorithms Compared
8
Smoothing
Classification Algorithm
Non-linear Smoothing
Linear Smoothing
9
An Example
Uniform Coding .83 bits/pel
Compound Coding .81 bits/pel
10

Conclusion

• Text and graphics have different characteristics
• Many methods of classifying blocks
• Spatial, DCT, Wavelets, DCT Codebooks
• Smoothing/post-processing
• Our methods compare favorably