Interpolation Techniques; Simulation & Analysis

1. CS 584 Multimedia Communication Interpolation Techniques; Simulation & Analysis Asmar Azar Khan 2005-06-0003

2. Objective • Study and analyze different interpolation techniques • Performance analysis of these techniques on different performance metrics • To propose a hardware based approach for Interpolation

3. Agenda • Introduction • Transcoding • Image Scaling • Literature Review • Interpolation Techniques • Linear • Nearest • Cubic • Spline • Proposed Algorithm • Filtering • Design Implementation • Memory Requirement • Delay Requirement • Future Proposals • Questions

4. Introduction • Interoperability of multimedia devices • Each device has different encoder and hence decoding schemes • Broadband TV and Video on demand • PDA and Mobile • Online Gaming • Internet Telephony • Role of Transcoders and Image Scaling

5. Transcoding • A steer demand of multimedia on network have given rise to challenges • Heterogonous Encoders and Decoders • Bit Rate ( video) • Delay ( voice and video) • Quality ( multimedia) • A technique where we change the encoded bit stream on the fly according to receiver compatibility • Encoded scheme • Error Correction Techniques • Spatial and Temporal Resolution

6. Image Scaling • Spatial Resolution of Image and Video • HDTV ( 1620 x 1200) etc… • PC Monitors ( 1280 x 800) XVGA • PDA (640 x 480) VGA • Mobile (176 x 144) QCIF

7. Literature Review • Software based approaches • Interpolation Techniques • Nearest Neighbor • Linear • Cubic Spline • Bicubic Spline • Hardware based Nearest Neighbor • Simulations results for Advanced Techniques • Recently DCT domain Interpolation has been presented

8. Interpolator Sampler Original Image Interpolation • Whenever an image is desired to be re-sampled • It is first interpolated to continuous image • Then the image is sampled Scaled Image

9. Interpolation Methods • Nearest Neighbor • Linear • Quadratic • Cubic B Spline • Normal

10. 1 , 0 < | x | < 0.5 0 , else Nearest Neighbor • Nearest Pixel Value • Less Complex • Low Quality • Edge handling Courtesy: Thomas M. Lehmann , Survey: Interpolation Methods in Medical Image Processing IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 18, NO. 11, NOVEMBER 1999 h(x) =

11. Linear Interpolation: Courtesy: Thomas M. Lehmann , Survey: Interpolation Methods in Medical Image Processing IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 18, NO. 11, NOVEMBER 1999 1 - | x | , 0 < | x | < 1 h(x) = 0 , else

12. Quadratic Interpolation Courtesy: Thomas M. Lehmann , Survey: Interpolation Methods in Medical Image Processing IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 18, NO. 11, NOVEMBER 1999 A1|x|2 + B1|x| + C1 , 0 < |x| <0.5 h(x) = A2|x|2 + B2|x| + C2 , 0.5 < |x| <1.5 0 , else

13. B-Spline Interpolation Courtesy: Thomas M. Lehmann , Survey: Interpolation Methods in Medical Image Processing IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 18, NO. 11, NOVEMBER 1999 1/2|x|3 -|x|2 + 2/3 , 0< |x| <1 h(x) = -(1/6)|x|3 + | x|2 -2|x| +4/3, 1< |x| <2 0 , else

14. Simulation • An image of 512 x 512 was taken • It was reduced to 256 x 256 • Then interpolated using different techniques 512 x 512 512 x 512 Down sampling Interpolation 256 x 256

15. Preprocessing; Low Pass Filter • A 7-tap filter used for CCIR-601 to SIF conversion [ -29 0 88 138 88 0 -29] * 1/256

16. Simulation; Test Image

17. SNR Calculation

18. Histogram Analysis

19. Nearest Neighbor

20. Linear Interpolation

21. Bicubic Interpolation

22. Spline Interpolation

23. Nearest Neighbor

24. Linear Interpolation

25. Bicubic Interpolation

26. Spline Interpolation

27. Fourier Transform of Original Image

28. Fourier Transform of Nearest Neighbor

29. Fourier Transform of Linear Interpolation twice scaling 16 14 12 10 8 6 4 2 0 -2 0 100 200 300 400 500 600 Fourier Transform of Linear

30. Fourier Transform of Bicubic

31. Fourier Transform of Spline

32. Re sampling Algorithm • Low pass filter is applied to avoid aliasing • Up sampling is done first in horizontal direction means column wise and then vertical direction i.e. row wise. • 40/11 is non integer factor • Up sample by 40 • Down sample by 11 • Similarly 10/3 factor

33. 640 176 144 480 11 40 3 10 Mapping onto scaled image There will be 768 blocks/slices of original and scaled image

34. Post Processing • A 7-tap filter used to convert SIF to CCIR-601 [-12 0 140 256 140 0 -12] * 1/256 • Removes the blocking effects from the interpolated image by introducing blurring • As nearest neighbor techniques introduces sudden changes due to boundary value problems

35. Controller based approach • Distributed memory architecture • State Machine based hardware • Pre processing filtering • Post processing filtering • Memory Read and Write

36. Distributed Memory Architecture Mem in Mem out Mem out Mem in Input Image Output Image Mem out Mem in Mem in Mem out

37. 11 to 40 Mapping

38. 3 to 10 Mapping

39. System Block Diagram

40. Future Proposals • Advanced Interpolation methods • Cubic Spline • Normal Spline • Generic Conversion • Generic scaling ratio

41. Questions !

42. I thank you for your time..