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True-Motion Estimation with 3-D Recursive Search Block Matching

True-Motion Estimation with 3-D Recursive Search Block Matching. Gerard de Haan, Paul W. A. C. Biezen Henk Huijgen Olukayode A. Ojo (Philips Research Laboratories, 5600 JA Eindhoven, the Netherlands.) This paper appears in: Circuits and Systems for Video Technology, IEEE Transactions on

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True-Motion Estimation with 3-D Recursive Search Block Matching

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  1. True-Motion Estimation with 3-D Recursive SearchBlock Matching Gerard de Haan, Paul W. A. C. Biezen Henk Huijgen Olukayode A. Ojo (Philips Research Laboratories, 5600 JA Eindhoven, the Netherlands.) This paper appears in: Circuits and Systems for Video Technology, IEEE Transactions on Page 368–379.388 ,Oct 1993

  2. Overview • Introduction • Recursive Search Method for True ME • 1-D Recursive Search • 2-D Recursive Search • 3-D Recursive Search • Updating Strategy • Further Emphasis on Smoothness • Block Erosion to Eliminate Blocking Effects • Evaluation Results & Experiments • Modified Mean Square Prediction Error(M2SE) • Smoothness • Conclusion

  3. Introduction • What is true motion? • Why do we find the true motion? • Consumer display scanrate conversion[1]-[8]. • Common drawback is decreased dynamic resolution. • Motion compensation techniques[9]-[12] are too expensive for consumer television applications.

  4. Overview • Introduction • Recursive Search Method for True ME • 1-D Recursive Search • 2-D Recursive Search • 3-D Recursive Search • Updating Strategy • Further Emphasis on Smoothness • Block Erosion to Eliminate Blocking Effects • Evaluation Results & Experiments • Modified Mean Square Prediction Error(M2SE) • Smoothness • Conclusion

  5. Recursive Search Method for True ME(1/5) • 1-D Recursive Search:similar to 2-D logarithmic search[22] • The candidate set (CSi) & prediction vector (Di-1): • Indicate with S rather than Di-1 as the spatial prediction vector • (pel-recursive algo. [23][24] ):

  6. Recursive Search Method for True ME(2/5) • 2-D Recursive Search: two spatial prediction vectors • A 1-D recursive algorithm cannot cope with discontinuities in the velocity plane. • Assumption (1): • The discontinuities in the velocity plane arespaced at a distance that enables convergence of the recursive block matcher in between two discontinuities. • Two estimators and the selection criterion: • As described in 1-DRS, updating, respectively, prediction vectors:

  7. Recursive Search Method for True ME(3/5) • 2-D Recursive Search solvesthe run-in problemat the boundaries of moving objects. • The best implementation of 2-DC results with predictions from blocks 1 and 3 for estimators a and b, respectively: where (X,Y) is the size of block.

  8. Recursive Search Method for True ME(4/5) • 3-D Recursive Search: temporal prediction vectors • Assumption (2): • The displacements between two consecutive velocity planes, due to movements in the picture, are small compared to the block size. • Rather than choosing the additional estimators c and d, applying temporal prediction vectors as additional candidates: • These convergence accelerators (CA) are taken from a block shifted diagonally over “ r ” blocks.

  9. Recursive Search Method for True ME(5/5) • 3-D RS candidate set CSa & CSb: • The CA's are particularly advantageous at the top of the screen, where the spatial process starts converging. • The CA's improve the temporal consistency.

  10. Overview • Introduction • Recursive Search Method for True ME • 1-D Recursive Search • 2-D Recursive Search • 3-D Recursive Search • Updating Strategy • Further Emphasis on Smoothness • Block Erosion to Eliminate Blocking Effects • Evaluation Results & Experiments • Modified Mean Square Prediction Error(M2SE) • Smoothness • Conclusion

  11. Updating Strategy 0improves the performance for small stationary image parts butdisturbs the convergence. • The asynchronous cyclic search (ACS): • Nbl is the output of a block counter • lut is a look-up table function • The pseudorandom look-up table (for p=9): symmetrical distribution around 0 with p updates

  12. Overview • Introduction • Recursive Search Method for True ME • 1-D Recursive Search • 2-D Recursive Search • 3-D Recursive Search • Updating Strategy • Further Emphasis on Smoothness • Block Erosion to Eliminate Blocking Effects • Evaluation Results & Experiments • Modified Mean Square Prediction Error(M2SE) • Smoothness • Conclusion

  13. Further Emphasis on Smoothness (1/2) • The risks which jeopardize the smoothness: • An element of the update sets may equal a multiple of the basic period of the structure. • "The other" estimator may not beconverged, or may be converged towrong value that does not correspond to the actual displacement. • Directly after a scene change, the convergence accelerators (CAs) yield the threatening candidate. • Improve the result for risks 1) & 3): • Add penalties to the error function related to the length of the difference vector between the candidates to be evaluated:

  14. Further Emphasis on Smoothness (2/2) • Respectively, 0.4%, 0.8%, and 1.6% of the maximum error value, for the cyclic update(Sn), the convergence accelerator (CA), and the fixed 0candidate vector. • The last candidate(0) especially requires a large penalty. • Improve the result for risk 2): • The situation occurs if a periodic part enters the picture from the blanking or appears from behind an other object. • Advantage of two independent estimators would be lost.

  15. Overview • Introduction • Recursive Search Method for True ME • 1-D Recursive Search • 2-D Recursive Search • 3-D Recursive Search • Updating Strategy • Further Emphasis on Smoothness • Block Erosion to Eliminate Blocking Effects • Evaluation Results & Experiments • Modified Mean Square Prediction Error(M2SE) • Smoothness • Conclusion

  16. Block Erosion to Eliminate Blocking Effects • Improve the result for: • Eliminating the visible block structures in the picture. • Eliminating fixed block boundaries from the vector field without blurring contours. • Finally assigned to the pixels in the quadrant: F H-1-1 E

  17. Overview • Introduction • Recursive Search Method for True ME • 1-D Recursive Search • 2-D Recursive Search • 3-D Recursive Search • Updating Strategy • Further Emphasis on Smoothness • Block Erosion to Eliminate Blocking Effects • Evaluation Results & Experiments • Modified Mean Square Prediction Error(M2SE) • Smoothness • Conclusion

  18. Evaluation Results& Experiments (1/4) • Modified Mean Square Prediction Error(M2SE):↓, quality↑ • s identifies the test sequence 1~5 • P . L is the number of pixels in the image excluding margin. • Smoothness Indicator: S(t)↑, smoothness↑ • Nb is the number of blocks in a field.

  19. Evaluation Results& Experiments (2/4) • Experiments:

  20. Evaluation Results& Experiments (3/4) Captured from: Frame Rate Up-Conversion,陳秉昱,January 8,2006

  21. Evaluation Results& Experiments (4/4) Captured from: Frame Rate Up-Conversion,陳秉昱,January 8,2006

  22. Conclusion • The newly designed motion estimation algorithm is emerging as the most attractive of the tested block-matching algorithms in the applicationof consumer field rate conversion. • The bidirectional convergence principle enabled combination of the conflicting demands for smoothness andyet steep edges in the velocity field. • Using new test criteria, the suitability of motion estimators fortelevision with motion compensated field rate doubling was tested.

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