Abstract
Motion estimation and compensation techniques are widely used for video coding applications but the real-time motion estimation is not easily achieved due to its enormous computations. In this paper, a new fast motion estimation algorithm based on line search is presented, in which computation complexity is greatly reduced by using the line search strategy and a parallel search pattern. Moreover, the accurate search is achieved because the small square search pattern is used. It has a best-case scenario of only 9 search points, which is 4 search points less than the diamond search algorithm. Simulation results show that, compared with the previous techniques, the LSPS algorithm significantly reduces the computational requirements for finding motion vectors, and also produces close performance in terms of motion compensation errors.
Highlights
The high correlation between successive frames can be exploited to improve coding efficiency, which is usually achieved by using motion estimation (ME) and motion compensation technology
Many ME methods have been studied in an effort to reduce the computational complexity of the ME, such as block matching algorithms (BMA), parametric/motion models, optical flow, and pel-recursive techniques
According to the above questions, this paper presents a novel line-square parallel search (LSPS) algorithm for suboptimal block ME
Summary
The high correlation between successive frames can be exploited to improve coding efficiency, which is usually achieved by using motion estimation (ME) and motion compensation technology. Many ME methods have been studied in an effort to reduce the computational complexity of the ME, such as block matching algorithms (BMA), parametric/motion models, optical flow, and pel-recursive techniques. Among these methods, BMA seems to be the most popular method due to its effectiveness and simplicity for both software and hardware implementations. According to the above questions, this paper presents a novel line-square parallel search (LSPS) algorithm for suboptimal block ME. Simulation results show that the LSPS algorithm is reducing computational complexity and improving the quality performance when compared to the DS algorithm.
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