Sejong University, DMS Lab. An Efficient True-Motion Estimator Using Candidate Vectors from a Parametric Motion Model Dong-kywn Kim 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

Sejong University, DMS Lab. 1 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

Sejong University, DMS Lab. 2 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

Sejong University, DMS Lab. 3 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

Sejong University, DMS Lab. 4 The 3-D Recursive Search Block Matcher (2/3) Motion Estimator

Sejong University, DMS Lab. 5 The 3-D Recursive Search Block Matcher (3/3) 3-D Recursive Search Block Matcher

Sejong University, DMS Lab. 6 Upgrading the 3-D RS Block-Matcher with a Parametric Candidate (1/2) Three & Four - Parameter Model

Sejong University, DMS Lab. 7 Upgrading the 3-D RS Block-Matcher with a Parametric Candidate (2/2) 3-D RS Parameter Model

Sejong University, DMS Lab. 8 Extraction of the Parameters from the Image Data (1/4) Position of the sample vectors in the image plane

Sejong University, DMS Lab. 9 Extraction of the Parameters from the Image Data (2/4) 18 dependent pairs

Sejong University, DMS Lab. 10 Extraction of the Parameters from the Image Data (3/4) Extraction of the parameters

Sejong University, DMS Lab. 11 Extraction of the Parameters from the Image Data (4/4) Check the reliability

Sejong University, DMS Lab. 12 Evaluation Of The Improvement (1/5) MSE

Sejong University, DMS Lab. 13 Evaluation Of The Improvement (2/5) Evaluation Method

Sejong University, DMS Lab. 14 Evaluation Of The Improvement (3/5) Sequence

Sejong University, DMS Lab. 15 Evaluation Of The Improvement (4/5) MSE Results

Sejong University, DMS Lab. 16 Evaluation Of The Improvement (5/5) Grey scale illustrating the horizontal vector component

Sejong University, DMS Lab. 17 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