Adaptive pattern selection strategy for diamond search algorithm in fast motion estimation
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In this paper, an adaptive pattern selection strategy for diamond search (DS) algorithm is proposed. DS is one of state-of-the-art motion estimation algorithms, while the fixed search strategy and the singular termination strategy lead to lots of redundancy of search points. The proposed search strategy is based on the observation that more than 75% motion vector differences are around the initial predicted search centre in the range of 1. In our search strategy, small diamond search pattern and large diamond search pattern are adaptively used according to the distribution of motion vector differences and the matching error information of initial search centre. Our search strategy focuses on how to use small diamond search pattern and large diamond search pattern more efficiently than diamond search algorithm without introducing additional search patterns. Experimental results show that the proposed algorithm can save about 10.81 search points and achieve 0.12 dB higher PSNR on average compared to DS.
KeywordsMotion estimation Adaptive pattern selection Diamond search algorithm Block-based matching algorithm
This work is supported in part by the Open Project Program of the State Key Lab of Novel Software Technology (Grant No. KFKT2016B14), Nanjing University, the Open Research Fund of CAS Key Laboratory of Spectral Imaging Technology (Grant No. LSIT201606D) and the Open Project Program of the National Laboratory of Pattern Recognition (NLPR) (Grant No. 201800030).
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