Performance of optical flow techniques
International Journal of Computer Vision
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Kriging as a surrogate fitness landscape in evolutionary optimization
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Faster convergence by means of fitness estimation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Lossy Strict Multilevel Successive Elimination Algorithm for Fast Motion Estimation
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Computational Optimization and Applications
Block Matching Algorithm Based on Particle Swarm Optimization for Motion Estimation
ICESS '08 Proceedings of the 2008 International Conference on Embedded Software and Systems
New pixel-decimation patterns for block matching in motion estimation
Image Communication
Fast block matching using prediction and rejection criteria
Signal Processing
A novel multi-threshold segmentation approach based on differential evolution optimization
Expert Systems with Applications: An International Journal
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
A neighborhood elimination approach for block matching in motion estimation
Image Communication
VLSI implementation of genetic four-step search for block matching algorithm
IEEE Transactions on Consumer Electronics
A new diamond search algorithm for fast block-matching motion estimation
IEEE Transactions on Image Processing
Adaptive rood pattern search for fast block-matching 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 simple and efficient search algorithm for block-matching motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
A lightweight genetic block-matching algorithm for video coding
IEEE Transactions on Circuits and Systems for Video Technology
Genetic motion search algorithm for video compression
IEEE Transactions on Circuits and Systems for Video Technology
Accuracy improvement and cost reduction of 3-step search block matching algorithm for video coding
IEEE Transactions on Circuits and Systems for Video Technology
A new three-step search algorithm for block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
Parameter optimization of PEMFC model with improved multi-strategy adaptive differential evolution
Engineering Applications of Artificial Intelligence
Information Sciences: an International Journal
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Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, block-matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame (macro block, MB) can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing the sum of absolute differences (SAD) produced by the MB of the current frame over a determined search window from the previous frame. The SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. The most straightforward BM method is the full search algorithm (FSA), which finds the most accurate motion vector, exhaustively calculating the SAD values for all the elements of the search window. Over this decade, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the cost of poor accuracy. In this paper, a new algorithm based on differential evolution (DE) is proposed to reduce the number of search locations in the BM process. To avoid computing several search locations, the algorithm estimates the SAD values (fitness) for some locations using the SAD values of previously calculated neighboring positions. As the proposed algorithm does not consider any fixed search pattern or any other different assumption, a high probability for finding the true minimum (accurate motion vector) is expected. In comparison with other fast BM algorithms, the proposed method deploys more accurate motion vectors, yet delivering competitive time rates.