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This process generates a new estimation of the motion vector for the block to be interpolated.
Block Matching Algorithms estimate motion on the basis of rectangular blocks and produce one motion vector for each block.
For convenience, we denote by the estimated frame between frame and frame and by the motion vector for the pixel location.
For any two neighboring frames, the motion vector for each macroblock (MB) is computed and the maximal motion vector can be obtained.
However, it is worth noticing that, in our JRVIR approach we do not provide redundant motion vector for all the inter macroblocks.
As mentioned above, the computation of motion vector for a smallest block shape, that is, 4 4 block, is the worst case for calculation.
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This architecture is based on thresholding of mutual information between successive frames for flagging frames requiring re-computation of saliency, and use of motion vectors for propagation of saliency values.
Figure 4 Neighboring motion vectors for weighted median vector filter.
Figure 3 shows of maximal motion vectors for the standard video sequence "Coastguard.qcif".qcif
Note that we choose multiple best motion vectors for each block.
Some of the mentioned methods utilize motion vectors or features from motion vectors for the recognition of periodic motion.
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