Pattern Based Motion Estimation using Zero Motion Pre-judgement and Quantum behaved Particle Swarm Optimization
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Abstract
Motion estimation is a fundamental and resource hungry operation in most of the video coding applications. The most popular method used in any video coding application is block matching motion estimation (BMME). This conventional fast motion estimation algorithm adopts a monotonic error surface for faster computation. However, these search techniques may trap at local minima resulting in erroneous motion estimation. To overcome this issue, various evolutionary swarm intelligence based algorithms were proposed. In this paper, a pattern based motion estimation using zero motion prejudgment and Quantum behaved Particle Swarm Optimization (QPSO) algorithms is proposed, referred to as the Pattern Based Motion Estimation (PBME) algorithm. The notion of QPSO improves the diversity in the search space, which enhances the search efficiency and helps in reduction of the computational burden. At the same time, QPSO needs fewer parameters to control. Therefore, the proposed algorithm enhances the estimation accuracy. An initial search pattern (Hexagonal Based Search) was used which speeds the convergence rate of the algorithm. From the simulation results, it was found that the proposed method outperformed the existing fast block matching (BMA) algorithms of the search point reduction by 40–75%
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