1. Texture Image Extrapolation for Compression
Sung-Won Yoon & SeongTaek Chung
Stanford University
December 4, 2000

2. Outline
Motivation : Recover the whole image using partial information of the image
Methods :
non-parametric sampling
estimation using subband decomposition of wavelet coefficients
estimation by induced correlation

3. Non-parametric Sampling
Based on the approach of Alexei A. Efros and Thomas K. Leung
 Method

4. Results Big Hole Scattered Holes Window size Filling order
Better performance
More data needed

5. Estimation Using Subband Decomposition
Spatial locality
Similarity between subbands
Estimate A from B by use of the mapping from C to B

6. Model One-to-four linear mapping
A. Pentland & B. Horowitz
Mapping between pairs of subbands are similar
Full search possible because repetitiveness of texture image

7. Results
Original
Detail level 1 zeroed
Detail level 1 estimated

8. Limitations Statistical differences in different subbands
Assumption of propagation of mapping does not hold in general
Very limited mapping information from lower subbands

9. Estimation by Induced Correlation
System Model

10. Results Original Estimated image 1 (PSNR: 15.33dB)
Interpolated image (PSNR : 12.73dB)

11. Conclusions Non-parametric sampling
window size and computation load
Estimation using subband decomposition
lack of mapping similarity between pairs of subbands
different statistical characteristics for different subbands
Estimation by induced correlation
optimal filter is hard to find