an efficient true-motion estimator using candidate vectors from a parametric motion model

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

ModelModel

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

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ContentsContents

IntroductionThe 3-D Recursive Search Block MatcherUpgrading the 3-D RS Block-Matcher with a Parametric CandidateExtraction of the Parameters from the Image DataEvaluation Of The ImprovementConclusion

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IntroductionIntroduction

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

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The 3-D Recursive Search Block MatcherThe 3-D Recursive Search Block Matcher

(1/3)(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

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The 3-D Recursive Search Block MatcherThe 3-D Recursive Search Block Matcher

(2/3)(2/3)

Motion Estimator

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The 3-D Recursive Search Block MatcherThe 3-D Recursive Search Block Matcher

(3/3)(3/3)

3-D Recursive Search Block Matcher

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Upgrading the 3-D RS Block-Matcher with Upgrading the 3-D RS Block-Matcher with a Parametric Candidatea Parametric Candidate(1/2)(1/2)

Three & Four - Parameter Model

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Upgrading the 3-D RS Block-Matcher with Upgrading the 3-D RS Block-Matcher with a Parametric Candidatea Parametric Candidate(2/2)(2/2)

3-D RS Parameter Model

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Extraction of the Parameters from Extraction of the Parameters from the Image Data the Image Data (1/4)(1/4)

Position of the sample vectors in the image plane

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Extraction of the Parameters from Extraction of the Parameters from the Image Data the Image Data (2/4)(2/4)

18 dependent pairs

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Extraction of the Parameters from Extraction of the Parameters from the Image Data the Image Data (3/4)(3/4)

Extraction of the parameters

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Extraction of the Parameters from Extraction of the Parameters from the Image Data the Image Data (4/4)(4/4)

Check the reliability

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Evaluation Of The Improvement Evaluation Of The Improvement (1/5)(1/5)

MSE

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Evaluation Of The Improvement Evaluation Of The Improvement (2/5)(2/5)

Evaluation Method

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Evaluation Of The Improvement Evaluation Of The Improvement (3/5)(3/5)

Sequence

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Evaluation Of The Improvement Evaluation Of The Improvement (4/5)(4/5)

MSE Results

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Evaluation Of The Improvement Evaluation Of The Improvement (5/5)(5/5)

Grey scale illustrating the horizontal vector component

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ConclusionConclusion

This paper introduced this “parametric candidate” in a very efficient (3-D recursive search) block-matching algorithmThese nine extracted motion vectors, it is possible to generate 18 sets of four parameters describing the camera motionIt showed that knowledge of the horizontal and vertical sampling densities could be used to judge the reliability of the modelIn 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