Fast motion estimation using small population-based modified parallel particle swarm optimisation
K. M. Bakwad, Shyam Sundar Pattnaik, Balwinder Singh Sohi, S. Devi, Sastry V. R. S. Gollapudi, Ch. Vidya Sagar, Prashanta Kumar Patra · International Journal of Parallel Emergent and Distributed Systems · 2011
This paper proposes a new variant of parallel particle swarm optimisation (PPSO) known as small population-based modified PPSO (SPMPPSO) for fast motion estimation. The proposed technique is used to reduce computational time for block motion estimation in video. In the said technique, the velocity and position equations of PPSO are modified to achieve adaptive step size for getting true motion vector. The new position of swarm depends on previous motion vector, time decreasing inertia weight and on time-varying acceleration coefficient. The best matching block is predicted by step size/position equation of SPMPPSO. The Von Neumann topology is used as search pattern in the SPMPPSO. In SPMPPSO, small population, i.e. five swarms with which two-step search are used to find best matching block. Zero motion prejudgement is used leads to faster convergence for getting the motion vector. The results of SPMPPSO are compared with those of PPSO and with those of other motion estimation algorithms. The limitations such as computational time, search parameter, initial search and search space are overcome in SPMPPSO. The proposed technique saves computational time up to 94% when compared with other published methods.