Loading…

A novel Cooperative Motion Estimation Algorithm based on Particle Swarm Optimization and its multicore implementation

A cooperative motion estimation (ME) scheme using a modified Particle Swarm Optimization (PSO) algorithm is presented. The proposed algorithm is based on a multi-swarm PSO model where a swarm of PSO particles is defined for each macroblock (MB) in the frame. Motion estimation is then performed in a...

Full description

Saved in:
Bibliographic Details
Published in:Signal processing. Image communication 2015-11, Vol.39 (Part A), p.121-140
Main Authors: Jalloul, Manal K., Al-Alaoui, Mohamad Adnan
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c336t-6e2649c47a44041b37cb761cf77eb435d78f3ad2a9e48a60419e5ae6b271f4343
cites cdi_FETCH-LOGICAL-c336t-6e2649c47a44041b37cb761cf77eb435d78f3ad2a9e48a60419e5ae6b271f4343
container_end_page 140
container_issue Part A
container_start_page 121
container_title Signal processing. Image communication
container_volume 39
creator Jalloul, Manal K.
Al-Alaoui, Mohamad Adnan
description A cooperative motion estimation (ME) scheme using a modified Particle Swarm Optimization (PSO) algorithm is presented. The proposed algorithm is based on a multi-swarm PSO model where a swarm of PSO particles is defined for each macroblock (MB) in the frame. Motion estimation is then performed in a cooperative manner concurrently for all the MBs in the frame. Cooperation between neighboring MBs during the motion estimation process is allowed through a communication step to exchange information about the motion vectors found so far in the estimation process. This synergic relationship between the swarms of adjacent MBs allows refining the motion search and leads to both a faster convergence of the PSO process and an improvement in the resulting motion vectors. Several techniques are also proposed to improve the search capacity and computational complexity of the PSO iterations. A novel PSO initialization scheme that exploits the existing temporal correlation is proposed to remove dependency between adjacent MBs. A fitness function history preservation mechanism is also presented to prevent redundant repeated calculations of the fitness function of a given search point by the PSO particles which dramatically decreases the computational complexity. Moreover, the maximum allowed velocity of the particles is adaptively varied during the PSO iterative process which provides a balance between search exploration and exploitation. The proposed scheme exhibits a high level of data parallelism since it is capable of performing motion estimation for all the MBs of the frame in parallel rather than serially. As a result, the presented algorithm is amenable to parallel processing techniques. In this paper, a multicore implementation of our proposed algorithm is performed using the MATLAB® Parallel Computing Toolbox™ (PCT). Extensive simulations are performed to analyze the performance of the presented algorithm. It is found that the presented scheme provides improvements in terms of accuracy and computational complexity as compared to conventional fast motion estimation techniques and two state-of-the-art PSO-based ME schemes. An analysis of the parallel performance shows that the presented scheme is highly scalable and that the parallel efficiency increases with the increase in video resolution. The multicore implementation of the proposed algorithm using MATLAB could achieve a speedup of 6.21 on eight CPU cores for high-definition (HD) video sequences. The multicore p
doi_str_mv 10.1016/j.image.2015.09.010
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1825477749</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0923596515001502</els_id><sourcerecordid>1825477749</sourcerecordid><originalsourceid>FETCH-LOGICAL-c336t-6e2649c47a44041b37cb761cf77eb435d78f3ad2a9e48a60419e5ae6b271f4343</originalsourceid><addsrcrecordid>eNp9kE1PxCAQhonRxPXjF3jh6KUVCoXl4GGz8SvRaKKeCaVTZdOWCuwa_fXirmdPM5k875vMg9AZJSUlVFysSjeYNygrQuuSqJJQsodmdC5VUQkp99GMqIoVtRL1ITqKcUUIqThRM7Re4NFvoMdL7ycIJrkN4AefnB_xVUy5drsu-jcfXHofcGMitDifnkxIzvaAnz9NGPDjlGH3vcPN2GKXIh7WfWZ8AOyGqYcBxrQFTtBBZ_oIp3_zGL1eX70sb4v7x5u75eK-sIyJVAioBFeWS8M54bRh0jZSUNtJCQ1ndSvnHTNtZRTwuREZUVAbEE0laccZZ8fofNc7Bf-xhpj04KKFvjcj-HXUdF7VXErJVUbZDrXBxxig01PI34cvTYn-laxXeitZ_0rWROksOacudynIX2wcBB2tg9FC6wLYpFvv_s3_APptiGA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1825477749</pqid></control><display><type>article</type><title>A novel Cooperative Motion Estimation Algorithm based on Particle Swarm Optimization and its multicore implementation</title><source>ScienceDirect Freedom Collection</source><creator>Jalloul, Manal K. ; Al-Alaoui, Mohamad Adnan</creator><creatorcontrib>Jalloul, Manal K. ; Al-Alaoui, Mohamad Adnan</creatorcontrib><description>A cooperative motion estimation (ME) scheme using a modified Particle Swarm Optimization (PSO) algorithm is presented. The proposed algorithm is based on a multi-swarm PSO model where a swarm of PSO particles is defined for each macroblock (MB) in the frame. Motion estimation is then performed in a cooperative manner concurrently for all the MBs in the frame. Cooperation between neighboring MBs during the motion estimation process is allowed through a communication step to exchange information about the motion vectors found so far in the estimation process. This synergic relationship between the swarms of adjacent MBs allows refining the motion search and leads to both a faster convergence of the PSO process and an improvement in the resulting motion vectors. Several techniques are also proposed to improve the search capacity and computational complexity of the PSO iterations. A novel PSO initialization scheme that exploits the existing temporal correlation is proposed to remove dependency between adjacent MBs. A fitness function history preservation mechanism is also presented to prevent redundant repeated calculations of the fitness function of a given search point by the PSO particles which dramatically decreases the computational complexity. Moreover, the maximum allowed velocity of the particles is adaptively varied during the PSO iterative process which provides a balance between search exploration and exploitation. The proposed scheme exhibits a high level of data parallelism since it is capable of performing motion estimation for all the MBs of the frame in parallel rather than serially. As a result, the presented algorithm is amenable to parallel processing techniques. In this paper, a multicore implementation of our proposed algorithm is performed using the MATLAB® Parallel Computing Toolbox™ (PCT). Extensive simulations are performed to analyze the performance of the presented algorithm. It is found that the presented scheme provides improvements in terms of accuracy and computational complexity as compared to conventional fast motion estimation techniques and two state-of-the-art PSO-based ME schemes. An analysis of the parallel performance shows that the presented scheme is highly scalable and that the parallel efficiency increases with the increase in video resolution. The multicore implementation of the proposed algorithm using MATLAB could achieve a speedup of 6.21 on eight CPU cores for high-definition (HD) video sequences. The multicore performance of the proposed scheme is also compared with existing parallel algorithms in the literature and is shown to give superior results. •Collaboration between neighboring MBs is allowed during motion estimation.•Cooperation between neighboring MBs allows refining motion vectors found so far.•Temporal correlation used in PSO initialization removes dependency between MBs.•The maximum allowed velocity of the PSO particles is adaptively varied.•An efficient multi-core implementation using Matlab is presented.</description><identifier>ISSN: 0923-5965</identifier><identifier>EISSN: 1879-2677</identifier><identifier>DOI: 10.1016/j.image.2015.09.010</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Algorithms ; Complexity ; Computation ; Cooperative multi-swarm ; Mathematical analysis ; Mathematical models ; Motion estimation ; Motion simulation ; Parallel processing ; PSO ; Searching ; Swarm intelligence</subject><ispartof>Signal processing. Image communication, 2015-11, Vol.39 (Part A), p.121-140</ispartof><rights>2015 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c336t-6e2649c47a44041b37cb761cf77eb435d78f3ad2a9e48a60419e5ae6b271f4343</citedby><cites>FETCH-LOGICAL-c336t-6e2649c47a44041b37cb761cf77eb435d78f3ad2a9e48a60419e5ae6b271f4343</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Jalloul, Manal K.</creatorcontrib><creatorcontrib>Al-Alaoui, Mohamad Adnan</creatorcontrib><title>A novel Cooperative Motion Estimation Algorithm based on Particle Swarm Optimization and its multicore implementation</title><title>Signal processing. Image communication</title><description>A cooperative motion estimation (ME) scheme using a modified Particle Swarm Optimization (PSO) algorithm is presented. The proposed algorithm is based on a multi-swarm PSO model where a swarm of PSO particles is defined for each macroblock (MB) in the frame. Motion estimation is then performed in a cooperative manner concurrently for all the MBs in the frame. Cooperation between neighboring MBs during the motion estimation process is allowed through a communication step to exchange information about the motion vectors found so far in the estimation process. This synergic relationship between the swarms of adjacent MBs allows refining the motion search and leads to both a faster convergence of the PSO process and an improvement in the resulting motion vectors. Several techniques are also proposed to improve the search capacity and computational complexity of the PSO iterations. A novel PSO initialization scheme that exploits the existing temporal correlation is proposed to remove dependency between adjacent MBs. A fitness function history preservation mechanism is also presented to prevent redundant repeated calculations of the fitness function of a given search point by the PSO particles which dramatically decreases the computational complexity. Moreover, the maximum allowed velocity of the particles is adaptively varied during the PSO iterative process which provides a balance between search exploration and exploitation. The proposed scheme exhibits a high level of data parallelism since it is capable of performing motion estimation for all the MBs of the frame in parallel rather than serially. As a result, the presented algorithm is amenable to parallel processing techniques. In this paper, a multicore implementation of our proposed algorithm is performed using the MATLAB® Parallel Computing Toolbox™ (PCT). Extensive simulations are performed to analyze the performance of the presented algorithm. It is found that the presented scheme provides improvements in terms of accuracy and computational complexity as compared to conventional fast motion estimation techniques and two state-of-the-art PSO-based ME schemes. An analysis of the parallel performance shows that the presented scheme is highly scalable and that the parallel efficiency increases with the increase in video resolution. The multicore implementation of the proposed algorithm using MATLAB could achieve a speedup of 6.21 on eight CPU cores for high-definition (HD) video sequences. The multicore performance of the proposed scheme is also compared with existing parallel algorithms in the literature and is shown to give superior results. •Collaboration between neighboring MBs is allowed during motion estimation.•Cooperation between neighboring MBs allows refining motion vectors found so far.•Temporal correlation used in PSO initialization removes dependency between MBs.•The maximum allowed velocity of the PSO particles is adaptively varied.•An efficient multi-core implementation using Matlab is presented.</description><subject>Algorithms</subject><subject>Complexity</subject><subject>Computation</subject><subject>Cooperative multi-swarm</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Motion estimation</subject><subject>Motion simulation</subject><subject>Parallel processing</subject><subject>PSO</subject><subject>Searching</subject><subject>Swarm intelligence</subject><issn>0923-5965</issn><issn>1879-2677</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNp9kE1PxCAQhonRxPXjF3jh6KUVCoXl4GGz8SvRaKKeCaVTZdOWCuwa_fXirmdPM5k875vMg9AZJSUlVFysSjeYNygrQuuSqJJQsodmdC5VUQkp99GMqIoVtRL1ITqKcUUIqThRM7Re4NFvoMdL7ycIJrkN4AefnB_xVUy5drsu-jcfXHofcGMitDifnkxIzvaAnz9NGPDjlGH3vcPN2GKXIh7WfWZ8AOyGqYcBxrQFTtBBZ_oIp3_zGL1eX70sb4v7x5u75eK-sIyJVAioBFeWS8M54bRh0jZSUNtJCQ1ndSvnHTNtZRTwuREZUVAbEE0laccZZ8fofNc7Bf-xhpj04KKFvjcj-HXUdF7VXErJVUbZDrXBxxig01PI34cvTYn-laxXeitZ_0rWROksOacudynIX2wcBB2tg9FC6wLYpFvv_s3_APptiGA</recordid><startdate>201511</startdate><enddate>201511</enddate><creator>Jalloul, Manal K.</creator><creator>Al-Alaoui, Mohamad Adnan</creator><general>Elsevier B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201511</creationdate><title>A novel Cooperative Motion Estimation Algorithm based on Particle Swarm Optimization and its multicore implementation</title><author>Jalloul, Manal K. ; Al-Alaoui, Mohamad Adnan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c336t-6e2649c47a44041b37cb761cf77eb435d78f3ad2a9e48a60419e5ae6b271f4343</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>Complexity</topic><topic>Computation</topic><topic>Cooperative multi-swarm</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Motion estimation</topic><topic>Motion simulation</topic><topic>Parallel processing</topic><topic>PSO</topic><topic>Searching</topic><topic>Swarm intelligence</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jalloul, Manal K.</creatorcontrib><creatorcontrib>Al-Alaoui, Mohamad Adnan</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Signal processing. Image communication</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jalloul, Manal K.</au><au>Al-Alaoui, Mohamad Adnan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A novel Cooperative Motion Estimation Algorithm based on Particle Swarm Optimization and its multicore implementation</atitle><jtitle>Signal processing. Image communication</jtitle><date>2015-11</date><risdate>2015</risdate><volume>39</volume><issue>Part A</issue><spage>121</spage><epage>140</epage><pages>121-140</pages><issn>0923-5965</issn><eissn>1879-2677</eissn><abstract>A cooperative motion estimation (ME) scheme using a modified Particle Swarm Optimization (PSO) algorithm is presented. The proposed algorithm is based on a multi-swarm PSO model where a swarm of PSO particles is defined for each macroblock (MB) in the frame. Motion estimation is then performed in a cooperative manner concurrently for all the MBs in the frame. Cooperation between neighboring MBs during the motion estimation process is allowed through a communication step to exchange information about the motion vectors found so far in the estimation process. This synergic relationship between the swarms of adjacent MBs allows refining the motion search and leads to both a faster convergence of the PSO process and an improvement in the resulting motion vectors. Several techniques are also proposed to improve the search capacity and computational complexity of the PSO iterations. A novel PSO initialization scheme that exploits the existing temporal correlation is proposed to remove dependency between adjacent MBs. A fitness function history preservation mechanism is also presented to prevent redundant repeated calculations of the fitness function of a given search point by the PSO particles which dramatically decreases the computational complexity. Moreover, the maximum allowed velocity of the particles is adaptively varied during the PSO iterative process which provides a balance between search exploration and exploitation. The proposed scheme exhibits a high level of data parallelism since it is capable of performing motion estimation for all the MBs of the frame in parallel rather than serially. As a result, the presented algorithm is amenable to parallel processing techniques. In this paper, a multicore implementation of our proposed algorithm is performed using the MATLAB® Parallel Computing Toolbox™ (PCT). Extensive simulations are performed to analyze the performance of the presented algorithm. It is found that the presented scheme provides improvements in terms of accuracy and computational complexity as compared to conventional fast motion estimation techniques and two state-of-the-art PSO-based ME schemes. An analysis of the parallel performance shows that the presented scheme is highly scalable and that the parallel efficiency increases with the increase in video resolution. The multicore implementation of the proposed algorithm using MATLAB could achieve a speedup of 6.21 on eight CPU cores for high-definition (HD) video sequences. The multicore performance of the proposed scheme is also compared with existing parallel algorithms in the literature and is shown to give superior results. •Collaboration between neighboring MBs is allowed during motion estimation.•Cooperation between neighboring MBs allows refining motion vectors found so far.•Temporal correlation used in PSO initialization removes dependency between MBs.•The maximum allowed velocity of the PSO particles is adaptively varied.•An efficient multi-core implementation using Matlab is presented.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.image.2015.09.010</doi><tpages>20</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0923-5965
ispartof Signal processing. Image communication, 2015-11, Vol.39 (Part A), p.121-140
issn 0923-5965
1879-2677
language eng
recordid cdi_proquest_miscellaneous_1825477749
source ScienceDirect Freedom Collection
subjects Algorithms
Complexity
Computation
Cooperative multi-swarm
Mathematical analysis
Mathematical models
Motion estimation
Motion simulation
Parallel processing
PSO
Searching
Swarm intelligence
title A novel Cooperative Motion Estimation Algorithm based on Particle Swarm Optimization and its multicore implementation
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T02%3A26%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20novel%20Cooperative%20Motion%20Estimation%20Algorithm%20based%20on%20Particle%20Swarm%20Optimization%20and%20its%20multicore%20implementation&rft.jtitle=Signal%20processing.%20Image%20communication&rft.au=Jalloul,%20Manal%20K.&rft.date=2015-11&rft.volume=39&rft.issue=Part%20A&rft.spage=121&rft.epage=140&rft.pages=121-140&rft.issn=0923-5965&rft.eissn=1879-2677&rft_id=info:doi/10.1016/j.image.2015.09.010&rft_dat=%3Cproquest_cross%3E1825477749%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c336t-6e2649c47a44041b37cb761cf77eb435d78f3ad2a9e48a60419e5ae6b271f4343%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1825477749&rft_id=info:pmid/&rfr_iscdi=true