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Therefore, motion smoothing is necessary.
Section 5 shows how motion smoothing is improved using the proposed algorithm of multiple motion models.
However, spatial motion smoothing is only effective at removing false vectors that occur as isolated pulse spikes.
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For DCVP, the parameters (e.g. search range, strength of the motion smoothing) are adapted to obtain the best average result in terms of RD performance and then the same parameters are used for OBDC.
In OBMC (which is the core of OBDC, see Figure 4) and in many similar motion estimation algorithms, smoothing is done on the motion field after its initial calculation.
Spatial motion vector smoothing was proposed to improve the performance of bidirectional MCTI in [8, 11].
One of these applications concerns the control of actuators of a fast ship for vertical motion smoothing: this is described as an example of EdROOM use.
A spatial motion smoothing filter is therefore used, similar to [11], but with the matching criterion defined in (3) and the PDWZ frame.
Section 3 shows how online motion smoothing can be formulated as a linear estimation problem with constant-velocity model that can be solved by Kalman filtering.
Most existing motion smoothing algorithms are offline smoothing.
As a result, the constrained motion smoothing could be solved efficiently by linear programming.
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