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A Fast Motion Estimation Using Prediction of Motion Estimation Error

Authors: Hyun Soo Kang 0001; Seong-Mo Park; Si-Woong Lee; Jae-Gark Choi; Byoung-Ju Yun;

A Fast Motion Estimation Using Prediction of Motion Estimation Error

Abstract

This paper presents a modified MSEA (multi-level successive elimination algorithm) which gives less computational complexity. We predict a motion estimation error using the norms at the already processed levels in the MSEA scheme and then decide on if the following levels should be proceeded using the predicted result. We skip the computation at the following levels where the processing is no longer meaningful. At this point, skipping the processing gives computational gain compared to the conventional MSEA scheme. For the purpose of predicting the norm at each level, we first show the theoretical analysis of the norm at each level and then verify the analysis by experiments. Based on the analysis, a new motion estimation method is proposed and its performance is evaluated.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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