Image Compression Using Inpainting

1. Image Compression Using Inpainting Min-Wook Jeong Young Min Kim Jungsuk Kwac

2. Organization • Motivation • Structure Inpainting • Texture Inpainting • Result • Conclusion • Reference

3. Motivation

4. Motivation (Cont.)

5. Structure Inpainting • Recovering general structure (big picture) • Regions devoid of severe variation • Flat areas, edge, large object and etc. • Solve PDE to inpaint missing area • Similar to the diffusion of the liquid given initial and boundary condition • Discrete implementation of PDE • Different inpainting methods for edges and flat areas

6. Structure Inpainting (Cont.)

7. Structure Inpainting (Cont.)

8. Texture Inpainting • Exploit similarity in statistical aspect with surrounding blocks • Marker, emblem, furs and etc. • Synthesize missing blocks by finding part in surrounding blocks that that best matches the missing area (using MMSE) • Computationally efficient method to fill in potentially complex region

9. Texture Inpainting (Cont.)

10. Texture Inpainting (Cont.)

11. Result

12. Revisited

13. Result

14. Result

15. Result

16. Conclusion • For some regions we have to strictly use either structure or texture inpainting. • For others, we have some freedom of choosing between two methods. • Texture inpainting can be done faster and structure inpainting gives better reconstruction.

17. Reference • M. Bertalmio, G. Sapiro, V. Caselles and C. Ballester, "Image Inpainting," SIGGRAPH 2000, pp. 417-422. • S. Rane, M. Bertalmio and G. Sapiro, "Structure and Texture Filling-in of missing image blocks for Wireless Transmission and for Compression Applications," IEEE Trans. Image Processing, Vol. 12, No. 3, pp. 296-303, March 2003.

18. Reference • Press, W.H., S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery, “Numerical Recipes in C 2/e”, Cambridge University Press, pp.827-856, 1992 • Kreyszig, E., “Advanced Engineering Mathematics 8/e”, John Wiley & Sons, Inc., pp.962-981, 1999

19. Thank You!