Journal of Global Optimization
Block Matching Algorithm Based on Particle Swarm Optimization for Motion Estimation
ICESS '08 Proceedings of the 2008 International Conference on Embedded Software and Systems
Influence of crossover on the behavior of Differential Evolution Algorithms
Applied Soft Computing
IEEE Transactions on Circuits and Systems for Video Technology
Novel directional gradient descent searches for fast block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
Differential Evolution Algorithm for MESFET Small Signal Model Parameter Extraction
ISED '10 Proceedings of the 2010 International Symposium on Electronic System Design
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new diamond search algorithm for fast block-matching motion estimation
IEEE Transactions on Image Processing
An efficient search strategy for block motion estimation using image features
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 fast adaptive motion estimation algorithm
IEEE Transactions on Circuits and Systems for Video Technology
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Motion Estimation (ME) is computationally expensive step in video encoding. Exhaustive search technique for ME yields maximum accuracy at the cost of highest execution time. To overcome the computational burden, many fast search algorithms are reported that limit the number of locations to be searched. ME is formulated as an optimization problem and the Sum of Absolute Difference (SAD) is considered as an objective function to be minimized. SAD error surface is a multimodal in nature. Fast searching algorithms converge to a minimal point rapidly but they may be trapped in local minima of SAD surface. This paper presents an application of Differential Evolution algorithm for motion estimation. The performance of the DE algorithm is compared with Full search, three step search, Diamond search and Particle swarm optimization for eight QCIF video sequences. Four performance indicators namely Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), number of search points and run time are considered for performance comparison of algorithms. Simulation result shows that both PSO and DE algorithms are performing close to Full search and reduces computational overload significantly in all the sequences.