1. Mark Kalman Isaac Keslassy Daniel Wang 12/6/00
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 => 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
<Do we need a slide or sth for the methodology before next slide?>
Distribution for DCT coefficient (2,1)

7. The Algorithms Compared
Talk About DCT Horizontal

8. Smoothing Classification Algorithm Non-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