Shape-Adaptive DCT for coding arbitrarily shaped objects

1. Shape-Adaptive DCT for coding arbitrarily shaped objects SrikanthRajagopalan ShantanuKurhekar Stanford University EE 398A Final Project

2. Overview • Motivation • SA-DCT • Implementation • Results • Analysis • Conclusion • Extension • References

3. Motivation • DCT – block transform • Edges not encoded differently • Can lead to blocking/ringing effect at edges • Edge distortion • Need to preserve edges or shape perfectly

4. SA-DCT • Boundary blocks encoded using shape-adaptive transform • In edge block, pixels belonging to background are ignored • Pixels belonging to foreground preserved by shifting to top-left • Overhead info (object mask, block type) required to be transmitted to decoder

5. Overall Algorithm 1. Divide image into blocks 2. Classify blocks : foreground, background or boundary 3. For foreground blocks, apply DCT 4. For boundary blocks, apply SA-DCT 5. Compress object mask and block type(overhead) 6. Entropy encode coefficients 7. Transmit coefficients and overhead 8. Reverse process at receiver to reconstruct image

6. SA-DCT implementation

7. Types of edge blocks

8. Overhead info • Edge mask: • 1 : boundary pixels • 0 : otherwise • Block type: • 0 : background blocks • 1 : boundary blocks • : foreground blocks • Only edge mask of boundary blocks and block • type are sent

9. Overhead info • Compressed using RLE and entropy encoded • Used at receiver to get object position in image • For image with large no of boundary blocks > overhead is larger • > more out-of-boundary pixels dropped by SA-DCT

10. Original image – Pyramid of Giza

11. Boundary image

12. Block-type classification

13. Up-down classification

14. Left-right classification

15. Reconstructed images SA-DCT DCT

16. Closer look at edges SA-DCT DCT

17. Original image - Pentagon

18. Reconstructed images SA-DCT DCT

19. Reconstructed images SA-DCT DCT

20. Original image – Tower of Pisa

21. Reconstructed images DCT SA-DCT

22. Rate -Distortion Performance Pentagon Pyramid Pisa • Rate for SA-DCT always better • PSNR comparable or higher at high Q step-size • PSNR lower at low Q step-size

23. Analysis • Higher Q step-size : SADCT better • Comparable PSNR, much better rate • No ringing effect at edges in SADCT • Edges are preserved • Lower Q step-size : DCT better • Preserves whole image • Blocking effect negligible • SADCT has lower PSNR since out-of-boundary pixels ignored

24. Very high quantization DCT SA-DCT

25. Very high quantization DCT SA-DCT

26. Conclusion • Use SADCT: • Low bit rate scenario • Maintain object shape • Background unimportant • Use DCT: • Overall image quality • Background important

27. Extension: Lagrangian Cost • Lagrangian cost used to decide whether SA-DCT or DCT should be applied on edge block • Block with large number of object pixels : DCT • Block with less number of object pixels : SA-DCT • Cost function : • J = D + λ R • where λ = 0.2 * q ^ 2

28. Extension: Lagrangian Cost Example SA-DCT DCT SA-DCT

29. References • T. Sikora and B. Makai, “Shape-adaptive DCT for generic coding of video,” IEEE Transactions on Circuits and Systems for Video Technology (CSVT), Feb. 1995 • W. Ng and Z. Lin, “A New Shape-Adaptive DCT for Coding Arbitrarily Shaped Image Segments,” IEEE, 2000 • G. K. Wallace, DEC, “The JPEG Still Picture Compression Standard,” IEEE Transactions on Consumer Electronics, December 1991

30. Thank You!