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A Hybrid Edge-Enhanced Motion Adaptive Deinterlacer

A Hybrid Edge-Enhanced Motion Adaptive Deinterlacer. By Marc Ramirez. Frame 1. Problem Statement & Motivation. Field A. Field B. Field C. Canon GL1 “Frame Mode” ~ 320 Vertical Lines Deinterlace from 60i into a 30p sequence

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A Hybrid Edge-Enhanced Motion Adaptive Deinterlacer

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  1. A Hybrid Edge-Enhanced Motion Adaptive Deinterlacer By Marc Ramirez

  2. Frame 1 Problem Statement & Motivation Field A Field B Field C • Canon GL1 “Frame Mode” ~ 320 Vertical Lines • Deinterlace from 60i into a 30p sequence • High Quality, Noise Reduction, Edge & Detail Preservation, Moderate Complexity • MC Recursive, VT Median, EDDI, BOB • Best Approach Depends on Material

  3. Initially Proposed Method Simonetti de Haan

  4. Actual Method

  5. Current DV Capture Cards Import 60i Sequences as Field-Merged 30p • Store First Three Fields (A,B,C) from Captured Frames1 & 2 • 2) Look at the SAD of Fields A & C, If <= Thresh1 -> Keep Original Frame * • Ex. Mounted Camera Recording a Stationary Object; White Background • 3) Detect Edges • *Could Potentially Cause Problems

  6. Two Types of Edge Detection • EDDI Horizontal Emphasis • Canny Method in Matlab Edge Function • - Smoothing By Gaussian Convolution • - 2D Derivative • - Ridge Tracking of Gradient Magnitude

  7. 4) Interpolate Along Found Edges - Step Through Known Lines Only - Pick a Test Block of Correct Length - Use SAD to Determine Best Match - If <= Thresh2 -> Interpolate - Use Nearest Neighbor if Between Pixels Known Known Known

  8. 5) Fill In Static Areas IF AND Fill In With Previous or Average of Pixel P&N

  9. 6) Detect If Slow Pixel Motion 7) Use Median Filter on Small Window B = SUM/|DIFF| for ( 4 Combinations) Med{E[A,F] E[B,E] E[C,D] E[G,H] lowB}

  10. 8) Spatially Interpolate Remaining High-Motion Pixels • 4 Tap Vertical Filter for Better Frequency Response • Might Also Include a Horizontal Component

  11. Conclusion/Future Changes • Overall the Implementation is Less Computationally Expensive than MC with Pretty Nice Results • The Algorithm Tries to Use the Proper Method Based on Simple Motion Detection • Many Threshold Parameters -> Difficult to Set the Correct Thresholds for All Cases • Could Later Implement EDDI Correctly on the Final Image • Future Method Could Incorporate Motion Estimation • Implement a Plug-in For Virtual Dub or AVISynth

  12. References [1] R. Simonetti, S. Carrato, G. Ramponi and A.Polo Filisan, 'Deinterlacing of HDTV Images for Multimedia Applications', in Signal Processing of HDTV, IV, E. Dubois and L. Chiariglione, Eds., Elsevier Science Publishers, 1993, pp. 765-772. [2] G. de Haan and E.B. Bellers, ‘Deinterlacing -- An overview', Proceedings of the IEEE, Vol. 86, No. 9, Sep. 1998, pp. 1839- 1857. [3] G. de Haan and R. Lodder, `De-interlacing of video data using motion vectors and edge Information', Digest of the ICCE'02, Jun. 2002, pp. 70-71. [4] G. de Haan, `Video processing for multimedia systems', ISBN: 90-9014015-8, Eindhoven Sep.  2000. [5] Y. Wang, J. Ostermann, and Y.Q. Zhang, ‘Video Processing and Communications’ Prentice Hall, 2002, ISBN 0-13-017547-1.

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