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An Efficient True-Motion Estimator Using Candidate Vectors from a Parametric Motion Model

An Efficient True-Motion Estimator Using Candidate Vectors from a Parametric Motion Model. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 8, NO. 1, FEBRUARY 1998 Gerard de Haan, Senior Member, IEEE, and Paul W. A. C. Biezen. Dong-kywn Kim. Contents. Introduction

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An Efficient True-Motion Estimator Using Candidate Vectors from a Parametric Motion Model

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  1. An Efficient True-Motion Estimator Using Candidate Vectors from a Parametric Motion Model IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 8, NO. 1, FEBRUARY 1998 Gerard de Haan, Senior Member, IEEE, and Paul W. A. C. Biezen Dong-kywn Kim

  2. Contents • Introduction • The 3-D Recursive Search Block Matcher • Upgrading the 3-D RS Block-Matcher with a Parametric Candidate • Extraction of the Parameters from the Image Data • Evaluation Of The Improvement • Conclusion

  3. Introduction • Motion Estimation Method- Try all possible vectors in a predefined range, to obtain the global optimum of the criterion function- Use one of the efficient approaches and test only a limited number of candidate vectors • Motion in Video Image- Object motion- Camera movements • Camera Motion- pan, tilt : uniform motion vector- zoom : Linearly changing- These types of motion can be described with a three parameter model • Propose- An Efficient True-Motion Estimator Using Candidate Vectors from a Parametric Motion Model

  4. The 3-D Recursive Search Block Matcher (1/3) • Advanced Motion Estimator- Quarter pel accuracy- Close to true-motion vector field- Relevant for scan rate conversion- The only single chip true-motion estimator • Form

  5. The 3-D Recursive Search Block Matcher (2/3) • Motion Estimator

  6. The 3-D Recursive Search Block Matcher (3/3) • 3-D Recursive Search Block Matcher

  7. Upgrading the 3-D RS Block-Matcher with a Parametric Candidate(1/2) • Three & Four - Parameter Model

  8. Upgrading the 3-D RS Block-Matcher with a Parametric Candidate(2/2) • 3-D RS Parameter Model

  9. Extraction of the Parameters from the Image Data (1/4) • Position of the sample vectors in the image plane

  10. Extraction of the Parameters from the Image Data (2/4) • 18 dependent pairs

  11. Extraction of the Parameters from the Image Data (3/4) • Extraction of the parameters

  12. Extraction of the Parameters from the Image Data (4/4) • Check the reliability

  13. Evaluation Of The Improvement (1/5) • MSE

  14. Evaluation Of The Improvement (2/5) • Evaluation Method

  15. Evaluation Of The Improvement (3/5) • Sequence

  16. Evaluation Of The Improvement (4/5) • MSE Results

  17. Evaluation Of The Improvement (5/5) • Grey scale illustrating the horizontal vector component

  18. Conclusion • This paper introduced this “parametric candidate” in a very efficient (3-D recursive search) block-matching algorithm • These nine extracted motion vectors, it is possible to generate 18 sets of four parameters describing the camera motion • It showed that knowledge of the horizontal and vertical sampling densities could be used to judge the reliability of the model • In the evaluation part of the paper a significant advantage, up to 50% reduction in MSE, was found on critical material applying the motion vectors for deinterlacing

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