Particle Swarm Optimization (PSO) is global optimization technique based on swarm intelligence. It simulates the behavior of bird flocking. It is widely accepted and focused by researchers due to its profound intelligence and simple algorithm structure. Currently PSO has been implemented in a wide range of research areas such as functional optimization, pattern recognition, neural network training and fuzzy system control etc.,. In video processing PSO is used to find the best matching block in Block matching algorithm, bit rate optimization for MPEG 1/2, object tracking and data clustering. In this paper the usage of PSO in Block matching algorithms for video compression is analyzed and the results are compared with the existing techniques.
| Published in |
Science Journal of Circuits, Systems and Signal Processing (Volume 3, Issue 6-1)
This article belongs to the Special Issue Computational Intelligence in Digital Image Processing |
| DOI | 10.11648/j.cssp.s.2014030601.13 |
| Page(s) | 17-23 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Motion Vector, Sum Absolute Difference, Average Mean Square Error, Full Search Algorithms, Particle Swarm Optimization, Directed Particle swarm Optimization, Block Matching Algorithm, Memetic PSO, Mutation Simplex PSO
| [1] | J. Kennedy, R.C Eberhart, “Particle swarm optimization”, in IEEE International Conference on Neural Networks,pp. 1942-1948, 1995. |
| [2] | J. Kennedy, R.C Eberhart, “A discrete binary version of the particle swarm optimization algorithm”, in IEEE International Conference on Neural Networks, Perth, Australia,pp. 4104-4108, 1997. |
| [3] | Y.Nie and K.K.Ma,”” Adaptive rood pattern search for fast block matching motion estimation”,IEEE Trans. Image Processing vol.11,No.12, PP.1442-1449 ,Dec. 2002. |
| [4] | Shan Zhu and Kai-Kuang Ma, “A New Diamond search algorithm for Fast Block Matching Motion Estimation”,IEEE Trans.Image Processing.Vol9 no.2 pp.287-290 ,February 2000. |
| [5] | C.H. Hsich, P.C. Lu, J.S. Shyn and E.H. Lu, “Motion estimation algorithm using interlock correlation”, IEEE Electronic Letters, vol. 5, pp. 276-277, 1990. |
| [6] | M. Ghanbari, “The cross-search algorithm for motion estimation”, IEEE Transaction on Communication, pp. 950-953, 1990. |
| [7] | K. Chow and M. Liou, “Genetic Motion search algorithm for video compression”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 6, pp. 440-445, 1993. |
| [8] | Xuedong Yuan,Xiaojing Shen “Block Matching Algorithm Based on Particle Swarm Optimization for motion estimation”, The 2008 International conference on embedded Software and Systems (ICESS2008),PP 191-197. |
| [9] | R. Ren, M. Manokar, Y. Shi, B. Zheng, “A Fast Block Matching Algorithm for Video Motion Estimation Based on Particle Swarm Optimization and Motion Prejudgment”, Proc. IEEE International Conference on Industrial and Information Systems. 2007. |
| [10] | D. Ranganadham, and P. Gorpuni, “An efficient bidirectional frame prediction using particle swarm optimization technique”, International Conference on Advances in Recent Technologies in Communication and Computing, 5328092, pp. 42-46, 2009. |
| [11] | H. Y. Fan and Y. Shi, “Study on Vmax of particle swarm optimization,” in Proc. Workshop Particle Swarm Opt., Indianapolis, IN, 2001. |
| [12] | F. van den Bergh, “An analysis of particle swarm optimizers,” Ph.D. dissertation, Dept. Comput. Sci., Univ. Pretoria, Pretoria, South Africa, 2002. |
| [13] | Thamarai M and Shanmugalakshmi R “‘Video coding using Directed Particle Swarm Optimization’ CiiT International Journal of Digital Image Processing, vol. 2, no. 8. pp. 2010. |
| [14] | Gorpuni PK, ‘Development of Fast motion Estimation Algorithms for Video compression’, M. Tech Thesis report. National Institute of Technology, Rourkela,2009. |
| [15] | Pooja Nakpal & Baghla S, ‘Video Compression by Memetic Algorithm’, International Journal of Advanced Computer Science Applications, vol. 2, no. 6, pp. 142-145, 2011 |
| [16] | Zhang Ping, Wei Ping, Yu Hong-yang & Fei Chun, ‘A Novel Search Algorithm Based on Particle Swarm Optimization and Simplex Method for Block Motion Estimation’, International Journal of Digital Content Technology and its Applications, vol. 5, no. 1, pp. 76-86, 2011 |
APA Style
Kakalakannan Damodharan, Thamarai Muthusamy. (2014). Analysis of Particle Swarm Optimization in Block Matching Algorithms for Video Coding. Science Journal of Circuits, Systems and Signal Processing, 3(6-1), 17-23. https://doi.org/10.11648/j.cssp.s.2014030601.13
ACS Style
Kakalakannan Damodharan; Thamarai Muthusamy. Analysis of Particle Swarm Optimization in Block Matching Algorithms for Video Coding. Sci. J. Circuits Syst. Signal Process. 2014, 3(6-1), 17-23. doi: 10.11648/j.cssp.s.2014030601.13
AMA Style
Kakalakannan Damodharan, Thamarai Muthusamy. Analysis of Particle Swarm Optimization in Block Matching Algorithms for Video Coding. Sci J Circuits Syst Signal Process. 2014;3(6-1):17-23. doi: 10.11648/j.cssp.s.2014030601.13
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.cssp.s.2014030601.13,
author = {Kakalakannan Damodharan and Thamarai Muthusamy},
title = {Analysis of Particle Swarm Optimization in Block Matching Algorithms for Video Coding},
journal = {Science Journal of Circuits, Systems and Signal Processing},
volume = {3},
number = {6-1},
pages = {17-23},
doi = {10.11648/j.cssp.s.2014030601.13},
url = {https://doi.org/10.11648/j.cssp.s.2014030601.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.s.2014030601.13},
abstract = {Particle Swarm Optimization (PSO) is global optimization technique based on swarm intelligence. It simulates the behavior of bird flocking. It is widely accepted and focused by researchers due to its profound intelligence and simple algorithm structure. Currently PSO has been implemented in a wide range of research areas such as functional optimization, pattern recognition, neural network training and fuzzy system control etc.,. In video processing PSO is used to find the best matching block in Block matching algorithm, bit rate optimization for MPEG 1/2, object tracking and data clustering. In this paper the usage of PSO in Block matching algorithms for video compression is analyzed and the results are compared with the existing techniques.},
year = {2014}
}
TY - JOUR T1 - Analysis of Particle Swarm Optimization in Block Matching Algorithms for Video Coding AU - Kakalakannan Damodharan AU - Thamarai Muthusamy Y1 - 2014/11/12 PY - 2014 N1 - https://doi.org/10.11648/j.cssp.s.2014030601.13 DO - 10.11648/j.cssp.s.2014030601.13 T2 - Science Journal of Circuits, Systems and Signal Processing JF - Science Journal of Circuits, Systems and Signal Processing JO - Science Journal of Circuits, Systems and Signal Processing SP - 17 EP - 23 PB - Science Publishing Group SN - 2326-9073 UR - https://doi.org/10.11648/j.cssp.s.2014030601.13 AB - Particle Swarm Optimization (PSO) is global optimization technique based on swarm intelligence. It simulates the behavior of bird flocking. It is widely accepted and focused by researchers due to its profound intelligence and simple algorithm structure. Currently PSO has been implemented in a wide range of research areas such as functional optimization, pattern recognition, neural network training and fuzzy system control etc.,. In video processing PSO is used to find the best matching block in Block matching algorithm, bit rate optimization for MPEG 1/2, object tracking and data clustering. In this paper the usage of PSO in Block matching algorithms for video compression is analyzed and the results are compared with the existing techniques. VL - 3 IS - 6-1 ER -
Information
Email: