Novel motion estimation algorithm for image stabilizer
Abstract
Purpose
The motion vector estimation algorithm is very widely used in many image process applications, such as the image stabilization and object tracking algorithms. The conventional searching algorithm, based on the block matching manipulation, is used to estimate the motion vectors in conventional image processing algorithms. During the block matching manipulation, the violent motion will result in greater amount of computation. However, too large amount of calculation will reduce the effectiveness of the motion vector estimation algorithm. This paper aims to present a novel searching method to estimate the motion vectors effectively.
Design/methodology/approach
This paper presents a novel searching method to estimate the motion vectors for high-resolution image sequences. The searching strategy of this algorithm includes three steps: the larger area searching, the adaptive directional searching and the small area searching.
Findings
The achievement of this paper is to develop a motion vector searching strategy to improve the computation efficiency. Compared with the conventional motion vector searching algorithms, the novel motion vector searching algorithm can reduce the motion matching manipulation effectively by 50 per cent.
Originality/value
This paper presents a novel searching strategy to estimate the motion vectors effectively. From the experimental results, the novel motion vector searching algorithm can reduce the motion matching manipulation effectively, compared with the conventional motion vector searching algorithms.
Keywords
Acknowledgements
The authors would thank the Ministry of Science and Technology (MOST) for the financial supports to the project (grant number: MOST 99-2218-E-224-002-).
Citation
Chang, S.-J. and Wang, R.-H. (2017), "Novel motion estimation algorithm for image stabilizer", Engineering Computations, Vol. 34 No. 1, pp. 77-89. https://doi.org/10.1108/EC-11-2015-0345
Publisher
:Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited