An efficient motion estimation algorithm based on particle swarm optimization
Asha Elizabeth Jacob,Immanuel Alex Pandian +1 more
- 01 Jan 2014
- Vol. 3, Iss: 3, pp 199-203
TL;DR: By using the PSO algorithm, the user could get a high accuracy in the block-based motion estimation by maintaining high estimation accuracy compared to the Full search method and Diamond search algorithm.
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Abstract: The PSO algorithm reduce the search points without the degradation of the image quality. It provides accurate motion estimation with very low complexity in the context of video estimation. This algorithm is capable of reducing the computational complexity of block matching process. This algorithm maintains high estimation accuracy compared to the full search method. The critical component in most block-based video compression system is Motion Estimation because redundancy between successive frames of video sequence allows for compression of video data. These algorithms are used to reduce the computational requirement by checking only some points inside the search window, while keeping a good error performance when compared with Full Search and Diamond search algorithm. This algorithm should maintain high estimation accuracy compared to the Full search method and Diamond search algorithm. Here by using the PSO algorithm could get a high accuracy in the block-based motion estimation.
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Citations
•Journal Article
Efficient block-based motion estimation architecture using particle swarm optimization.
TL;DR: An efficient block-based ME architecture, in which the motion vectors are obtained by searching for the best match in the previous frame, based on applying the global search ability of Particle Swarm Optimization that reduces the number of logic elements.
1
•Dissertation
Devolopment of Mean and Median Based Adaptive Search Algorithm for Motion Estimation in SNR Scalable Video Coding
Rajkumar Maharaju
- 01 Jun 2015
TL;DR: In this paper, a particle swarm optimization (PSO) algorithm is used to estimate the motion with very low complexity in the context of video estimation, which is a method of computation aims at optimizing a problem with the help of popular candidate solutions.
Pattern Search Based on Particle Swarm Optimization Technique for Block Matching Motion Estimation Algorithm
Siti Eshah Che Osman,Hamidah Jantan +1 more
- 27 Nov 2017
TL;DR: A new enhanced algorithm using a pattern based particle swarm optimization (PSO) has been proposed for obtaining least number of computations and to give better estimation accuracy.
References
A new diamond search algorithm for fast block-matching motion estimation
Shan Zhu,Kai-Kuang Ma +1 more
TL;DR: Experimental results show that the proposed diamond search (DS) algorithm is better than the four-step search (4SS) and block-based gradient descent search (BBGDS), in terms of mean-square error performance and required number of search points.
2K
Comparison between Genetic Algorithms and Particle Swarm Optimization
Russell C. Eberhart,Yuhui Shi +1 more
TL;DR: This paper compares two evolutionary computation paradigms: genetic algorithms and particle swarm optimization, and suggests ways in which performance might be improved by incorporating features from one paradigm into the other.
1.7K
An efficient three-step search algorithm for block motion estimation
Xuan Jing,Lap-Pui Chau +1 more
TL;DR: A modification on the three-step search algorithm which employs a small diamond pattern in the first step, and the unrestricted search step is used to search the center area and shows better results in terms of MSE and requires less computation by up to 15% on average.
233
On fast and accurate block-based motion estimation algorithms using particle swarm optimization
Jing Cai,W. David Pan +1 more
TL;DR: This paper proposed a new block matching algorithm based on a set of strategies adapted from the standard particle swarm optimization approach that could achieve significant improvements over leading fast block matching methods including the diamond search and the cross-diamond search methods, in terms of both estimation accuracy and computational cost.
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