An efficient three-step search algorithm for block motion estimation
IEEE Transactions on Multimedia
Adaptive Double-Layered Initial Search Pattern for Fast Motion Estimation
IEEE Transactions on Multimedia
A novel four-step search algorithm for fast block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
A block-based gradient descent search algorithm for block motion estimation in video coding
IEEE Transactions on Circuits and Systems for Video Technology
A novel unrestricted center-biased diamond search algorithm for block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
Computation reduction for motion search in low rate video coders
IEEE Transactions on Circuits and Systems for Video Technology
A novel cross-diamond search algorithm for fast block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
Overview of the H.264/AVC video coding standard
IEEE Transactions on Circuits and Systems for Video Technology
An effective variable block-size early termination algorithm for H.264 video coding
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
A content-motion-aware motion estimation for quality-stationary video coding
EURASIP Journal on Advances in Signal Processing
WSEAS Transactions on Systems and Control
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Center-biased fast motion estimation algorithms, e.g., block-based gradient descent search and diamond search, can perform much better than coarse-to-fine search algorithms, such as 2-D logarithmic search and three-step search. The latter type of algorithms, however, is more suitable for handling large motion content. To combine the advantages of both types of algorithms, an adaptive algorithm performing search patterns switching (SPS) is proposed in this paper. The proposed SPS algorithm classifies the motion content of a block using a simple yet efficient motion content classifier called error descent rate. Unlike other classifiers with heavy overhead, this classifier requires only the searching of a few points in the search window and then a division operation. Experimental results show that the proposed SPS algorithm is very robust.