Neighbourhood-blocks motion vector estimation technique using pyramidal data structure

A pyramidal motion estimation technique that makes use of the motion correlation within a pyramidal level is proposed. In the proposed technique, motion vectors from neighbouring motion blocks are taken into consideration as possible candidates. This is done in lieu of scaling the motion vectors from the corresponding positions at the adjacent lower pyramidal level as the prediction motion vectors for the current pyramidal level (as performed in the conventional technique). Each of these candidate motion vectors is used as the prediction motion vector and refined, and the one that has the least matching distortion is chosen as the motion vector at the current pyramidal level. Compared to the conventional pyramidal motion estimation technique, the proposed method effectively overcomes the problem of propagation of false motion vectors. Simulation studies show that a substantial improvement is achieved in the performance, both in terms of the prediction mean square error and the number of coding bits for the motion vectors.