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Most existing motion smoothing algorithms are offline smoothing.
This paper is organized as follows: Section 2 reviews previous motion smoothing algorithms and related estimation background.
Compared to previous adaptive Kalman-filtering-based motion smoothing algorithms, the proposed dynamic multiple-model estimation is able to choose the proper parameters optimally from a probabilistic viewpoint.
Table 1 Numerical comparison between different motion smoothing algorithms for the synthetic camera motion Mean square jitter Mean square acceleration Unsmoothed 314.00 2217.43 Small σ p 11.58 21.79 Large σ p 19.24 28.09 Proposed 3.93 10.80.
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As a result, the motion smoothing algorithm should be adaptive accordingly.
This is a hard constraint that has to be considered in the camera motion smoothing algorithm.
We propose an online motion smoothing algorithm that resolves both problems.
Section 5 shows how motion smoothing is improved using the proposed algorithm of multiple motion models.
Adaptive algorithms were proposed for online motion smoothing to resolve this problem.
Then, spatial motion smoothing and motion refinement is performed and an adaptive overlapped block motion compensation algorithm is applied to a few selected neighboring motion vectors.
Therefore, motion smoothing is necessary.
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