Video Processing and Communications
Video Processing and Communications
Video Coding for Mobile Communications: Efficiency, Complexity, and Resillience
Video Coding for Mobile Communications: Efficiency, Complexity, and Resillience
Motion compensation of motion vectors
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
Fast block matching algorithm based on the winner-update strategy
IEEE Transactions on Image Processing
Successive elimination algorithm for motion estimation
IEEE Transactions on Image Processing
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 fast hierarchical motion vector estimation algorithm using mean pyramid
IEEE Transactions on Circuits and Systems for Video Technology
Generalized information potential criterion for adaptive system training
IEEE Transactions on Neural Networks
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Efficient motion estimation is an important problem because it determines the compression efficiency and complexity of a video encoder. Motion estimation can be formulated as an optimization problem; most motion estimation algorithms use mean squared error, sum of absolute differences or maximum a posteriori probability as the optimization criterion and apply search-based techniques (e.g., exhaustive search or three-step search) to find the optimum motion vector. However, most of these algorithms do not effectively utilize the knowledge gained in the search process for future search efforts and hence are computationally inefficient. This paper addresses this inefficiency problem by introducing an adaptive motion estimation scheme that substantially reduces computational complexity while yet providing comparable compression efficiency, as compared to existing fast-search methods. Our approach is motivated by the recent developments in using Renyi's entropy as the optimization criterion for system modeling [D. Erdogmus, J. Principe, Comparison of entropy and mean square error criteria in adaptive system training using higher order, in: Proceedings of the International Workshop on Independent Component Analysis and Signal Separation, Helsinki, Finland, 2000, pp. 75-80]. This scheme is particularly suited for wireless video sensor networks, video conferencing systems and live streaming videos which have stringent computational requirements. Our results show that our scheme reduces the computational complexity by a factor of 9-21, compared to the existing fast algorithms.