Fast Motion Estimation Using Spatio-temporal Correlations
Motion Estimation (ME) is an important part of video encoding systems, since it can significantly affect the output quality of an encoded sequences. However, ME requires a significant part of the encoding time, because ME is a combination of techniques such as motion starting point, motion search pa...
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| creator | Yoon, Hyo Sun Yoo, Jae Myeong Dinh, Toan Nguyen Son, Hwa Jeong Park, Mi Seon Lee, Guee Sang |
| description | Motion Estimation (ME) is an important part of video encoding systems, since it can significantly affect the output quality of an encoded sequences. However, ME requires a significant part of the encoding time, because ME is a combination of techniques such as motion starting point, motion search pattern, etc. For this reason, low complexity motion estimation algorithms are viable solutions. In this paper, we propose a motion estimation algorithm to find the most accurate motion vectors(MVs) with the aim to maximize the encoding speed as well as the image quality. The proposed algorithm takes advantage of spatio-temporal correlations to decide the search pattern and the search start point adaptively and to avoid unnecessary motion vector search. Experiments show that the speedup improvement of the proposed algorithm over Motion Vector Field Adaptive Search Technique (MVFAST) and Predictive Motion Vector Fiekd Adaptive Search Technique (PMVFAST) can be up to 1.5 ~ 8 times faster while maintaining very similar image quality. |
| doi_str_mv | 10.1007/11941354_56 |
| format | Conference Proceeding |
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The proposed algorithm takes advantage of spatio-temporal correlations to decide the search pattern and the search start point adaptively and to avoid unnecessary motion vector search. Experiments show that the speedup improvement of the proposed algorithm over Motion Vector Field Adaptive Search Technique (MVFAST) and Predictive Motion Vector Fiekd Adaptive Search Technique (PMVFAST) can be up to 1.5 ~ 8 times faster while maintaining very similar image quality.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540497769</identifier><identifier>ISBN: 3540497765</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540497790</identifier><identifier>EISBN: 354049779X</identifier><identifier>DOI: 10.1007/11941354_56</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Computer systems and distributed systems. User interface ; Exact sciences and technology ; Motion Estimation ; Pattern recognition. Digital image processing. 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H.</contributor><contributor>Saito, Hideo</contributor><contributor>Haller, Michael</contributor><contributor>Cheok, Adrian</contributor><creatorcontrib>Yoon, Hyo Sun</creatorcontrib><creatorcontrib>Yoo, Jae Myeong</creatorcontrib><creatorcontrib>Dinh, Toan Nguyen</creatorcontrib><creatorcontrib>Son, Hwa Jeong</creatorcontrib><creatorcontrib>Park, Mi Seon</creatorcontrib><creatorcontrib>Lee, Guee Sang</creatorcontrib><title>Fast Motion Estimation Using Spatio-temporal Correlations</title><title>Advances in Artificial Reality and Tele-Existence</title><description>Motion Estimation (ME) is an important part of video encoding systems, since it can significantly affect the output quality of an encoded sequences. However, ME requires a significant part of the encoding time, because ME is a combination of techniques such as motion starting point, motion search pattern, etc. For this reason, low complexity motion estimation algorithms are viable solutions. In this paper, we propose a motion estimation algorithm to find the most accurate motion vectors(MVs) with the aim to maximize the encoding speed as well as the image quality. The proposed algorithm takes advantage of spatio-temporal correlations to decide the search pattern and the search start point adaptively and to avoid unnecessary motion vector search. Experiments show that the speedup improvement of the proposed algorithm over Motion Vector Field Adaptive Search Technique (MVFAST) and Predictive Motion Vector Fiekd Adaptive Search Technique (PMVFAST) can be up to 1.5 ~ 8 times faster while maintaining very similar image quality.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Computer systems and distributed systems. User interface</subject><subject>Exact sciences and technology</subject><subject>Motion Estimation</subject><subject>Pattern recognition. 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Computational geometry</subject><subject>Saptial Correlation</subject><subject>Software</subject><subject>Temporal Correlation</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540497769</isbn><isbn>3540497765</isbn><isbn>9783540497790</isbn><isbn>354049779X</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpVkDtPw0AQhI-XhAmp-ANuKCgMu_f0lihKACmIAlKfztY5Mji2deeGf88loYBqZzWfdlbD2A3CPQKYB0SSKJS0Sp-wOZkyaZBkDMEpy1AjFkJIOvvnaTpnGQjgBRkpLtlVjJ8AwA3xjNHKxSl_HaZ26PNlnNqdO8hNbPtt_j7ut2Lyu3EIrssXQwi-OxDxml00rot-_jtnbLNafiyei_Xb08vicV2MHGkqeEoilClNOW0cVxUoX6P0RjnytayNU6hFo7grDXhdKxJkKqNKAKqqUszY7fHu6GLtuia4vm6jHUN6NXxbJCKuUCXu7sjFZPVbH2w1DF_RIth9d_ZPd-IHZqFZ9g</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Yoon, Hyo Sun</creator><creator>Yoo, Jae Myeong</creator><creator>Dinh, Toan Nguyen</creator><creator>Son, Hwa Jeong</creator><creator>Park, Mi Seon</creator><creator>Lee, Guee Sang</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2006</creationdate><title>Fast Motion Estimation Using Spatio-temporal Correlations</title><author>Yoon, Hyo Sun ; Yoo, Jae Myeong ; Dinh, Toan Nguyen ; Son, Hwa Jeong ; Park, Mi Seon ; Lee, Guee Sang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p219t-20029142795a67a25b05ec14e75a9ec4c7a5163f52a870e6c59397b758009bb83</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Computer systems and distributed systems. User interface</topic><topic>Exact sciences and technology</topic><topic>Motion Estimation</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Saptial Correlation</topic><topic>Software</topic><topic>Temporal Correlation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yoon, Hyo Sun</creatorcontrib><creatorcontrib>Yoo, Jae Myeong</creatorcontrib><creatorcontrib>Dinh, Toan Nguyen</creatorcontrib><creatorcontrib>Son, Hwa Jeong</creatorcontrib><creatorcontrib>Park, Mi Seon</creatorcontrib><creatorcontrib>Lee, Guee Sang</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yoon, Hyo Sun</au><au>Yoo, Jae Myeong</au><au>Dinh, Toan Nguyen</au><au>Son, Hwa Jeong</au><au>Park, Mi Seon</au><au>Lee, Guee Sang</au><au>Pan, Zhigeng</au><au>Liang, Ronghua</au><au>Lau, Rynson W. H.</au><au>Saito, Hideo</au><au>Haller, Michael</au><au>Cheok, Adrian</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Fast Motion Estimation Using Spatio-temporal Correlations</atitle><btitle>Advances in Artificial Reality and Tele-Existence</btitle><date>2006</date><risdate>2006</risdate><spage>548</spage><epage>556</epage><pages>548-556</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540497769</isbn><isbn>3540497765</isbn><eisbn>9783540497790</eisbn><eisbn>354049779X</eisbn><abstract>Motion Estimation (ME) is an important part of video encoding systems, since it can significantly affect the output quality of an encoded sequences. However, ME requires a significant part of the encoding time, because ME is a combination of techniques such as motion starting point, motion search pattern, etc. For this reason, low complexity motion estimation algorithms are viable solutions. In this paper, we propose a motion estimation algorithm to find the most accurate motion vectors(MVs) with the aim to maximize the encoding speed as well as the image quality. The proposed algorithm takes advantage of spatio-temporal correlations to decide the search pattern and the search start point adaptively and to avoid unnecessary motion vector search. Experiments show that the speedup improvement of the proposed algorithm over Motion Vector Field Adaptive Search Technique (MVFAST) and Predictive Motion Vector Fiekd Adaptive Search Technique (PMVFAST) can be up to 1.5 ~ 8 times faster while maintaining very similar image quality.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11941354_56</doi><tpages>9</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Advances in Artificial Reality and Tele-Existence, 2006, p.548-556 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_19992515 |
| source | Springer Books; IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Computer systems and distributed systems. User interface Exact sciences and technology Motion Estimation Pattern recognition. Digital image processing. Computational geometry Saptial Correlation Software Temporal Correlation |
| title | Fast Motion Estimation Using Spatio-temporal Correlations |
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