High Quality Compression of Educational Videos Using Content-Adaptive Framework
Identifieur interne : 000E39 ( Istex/Corpus ); précédent : 000E38; suivant : 000E40High Quality Compression of Educational Videos Using Content-Adaptive Framework
Auteurs : Ankush Mittal ; Ankur Jain ; Sourabh Jain ; Sumit GuptaSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2006.
Abstract
Abstract: With multimedia E-learning becoming popular, better compression algorithms are required that are space efficient but maintain the quality of video data, particularly slide and blackboard text. In this paper, an educational video compression technique is presented that dynamically allocates the space according to the content importance of each video segment in the educational videos. We present a phase correlation based motion estimation and compensation algorithm to encode important moving objects in efficient manner. Temporal coherence is exploited in a two phase manner. First, the frames with high similarity are categorized and encoded efficiently. Secondly, the compression ratio is adapted according to the frame content. The algorithm is compared with the state-of-the-art standards such as H.261, MPEG-4, etc. on large database. The comparison shows that for similar bit rates, the video quality for our algorithm is significantly better than the other methods.
Url:
DOI: 10.1007/11612704_93
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ISTEX:D3C0039875BB200361056E3AF6A51DF36B928DDDLe document en format XML
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In this paper, an educational video compression technique is presented that dynamically allocates the space according to the content importance of each video segment in the educational videos. We present a phase correlation based motion estimation and compensation algorithm to encode important moving objects in efficient manner. Temporal coherence is exploited in a two phase manner. First, the frames with high similarity are categorized and encoded efficiently. Secondly, the compression ratio is adapted according to the frame content. The algorithm is compared with the state-of-the-art standards such as H.261, MPEG-4, etc. on large database. The comparison shows that for similar bit rates, the video quality for our algorithm is significantly better than the other methods.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Ankush Mittal</name><affiliations><json:string>Department of Computer Science and Enginnering, Indian Institute of Technology, 247667, Roorkee, UA, India</json:string><json:string>E-mail: ankumfec@iitr.ernet.in</json:string></affiliations></json:item><json:item><name>Ankur Jain</name><affiliations><json:string>Department of Computer Science and Enginnering, Indian Institute of Technology, 247667, Roorkee, UA, India</json:string><json:string>E-mail: jainnuec@iitr.ernet.in</json:string></affiliations></json:item><json:item><name>Sourabh Jain</name><affiliations><json:string>Department of Computer Science and Enginnering, Indian Institute of Technology, 247667, Roorkee, UA, India</json:string><json:string>E-mail: soravuec@iitr.ernet.in</json:string></affiliations></json:item><json:item><name>Sumit Gupta</name><affiliations><json:string>Department of Computer Science and Engg., MVGR College of Engg., 535005, Vizianagaram, A.P., India</json:string><json:string>E-mail: sumitfec@iitr.ernet.in</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: With multimedia E-learning becoming popular, better compression algorithms are required that are space efficient but maintain the quality of video data, particularly slide and blackboard text. 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In this paper, an educational video compression technique is presented that dynamically allocates the space according to the content importance of each video segment in the educational videos. We present a phase correlation based motion estimation and compensation algorithm to encode important moving objects in efficient manner. Temporal coherence is exploited in a two phase manner. First, the frames with high similarity are categorized and encoded efficiently. Secondly, the compression ratio is adapted according to the frame content. The algorithm is compared with the state-of-the-art standards such as H.261, MPEG-4, etc. on large database. The comparison shows that for similar bit rates, the video quality for our algorithm is significantly better than the other methods.</p></abstract><textClass><keywords scheme="Book Subject Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book Subject Group"><list><label>I</label><label>I22005</label><label>I2203X</label><label>I22021</label><label>I21017</label><label>I16021</label><item><term>Computer Science</term></item><item><term>Computer Imaging, Vision, Pattern Recognition and Graphics</term></item><item><term>Pattern Recognition</term></item><item><term>Image Processing and Computer Vision</term></item><item><term>Artificial Intelligence (incl. 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University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgName>University of Surrey</OrgName><OrgAddress><City>Guildford</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgName>Cornell University</OrgName><OrgAddress><City>Ithaca</City><State>NY</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff5"><OrgName>ETH Zurich</OrgName><OrgAddress><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff6"><OrgName>Stanford University</OrgName><OrgAddress><City>CA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgName>Weizmann Institute of Science</OrgName><OrgAddress><City>Rehovot</City><Country>Israel</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><City>MA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>New York University</OrgName><OrgAddress><City>NY</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbruecken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-540-32432-4</BookID><BookTitle>Computer Vision – ACCV 2006</BookTitle><BookSubTitle>7th Asian Conference on Computer Vision, Hyderabad, India, January 13-16, 2006. Proceedings, Part II</BookSubTitle><BookVolumeNumber>3852</BookVolumeNumber><BookSequenceNumber>3852</BookSequenceNumber><BookDOI>10.1007/11612704</BookDOI><BookTitleID>128860</BookTitleID><BookPrintISBN>978-3-540-31244-4</BookPrintISBN><BookElectronicISBN>978-3-540-32432-4</BookElectronicISBN><BookChapterCount>96</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2006</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I22005" Priority="1" Type="Secondary">Computer Imaging, Vision, Pattern Recognition and Graphics</BookSubject><BookSubject Code="I2203X" Priority="2" Type="Secondary">Pattern Recognition</BookSubject><BookSubject Code="I22021" Priority="3" Type="Secondary">Image Processing and Computer Vision</BookSubject><BookSubject Code="I21017" Priority="4" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><BookSubject Code="I16021" Priority="5" Type="Secondary">Algorithm Analysis and Problem Complexity</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>P.</GivenName><GivenName>J.</GivenName><FamilyName>Narayanan</FamilyName></EditorName><Contact><Email>pjn@iiit.ac.in</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Shree</GivenName><GivenName>K.</GivenName><FamilyName>Nayar</FamilyName></EditorName><Contact><Email>nayar@cs.columbia.edu</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Heung-Yeung</GivenName><FamilyName>Shum</FamilyName></EditorName><Contact><Email>hshum@microsoft.com</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>Center for Visual Information Technology</OrgDivision><OrgName>International Institute of Information Technology</OrgName><OrgAddress><City>Hyderabad</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgDivision>Department of Computer Science</OrgDivision><OrgName>Columbia University</OrgName><OrgAddress><Street>500 West 120th Street</Street><Postcode>NY 10027</Postcode><City>New York</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgName>Microsoft Research Asia</OrgName><OrgAddress><Postbox>P.O. Box</Postbox><City>Beijing</City><Country>P.R. China</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part7"><PartInfo TocLevels="0"><PartID>7</PartID><PartSequenceNumber>7</PartSequenceNumber><PartTitle>Poster Session 3</PartTitle><PartChapterCount>42</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Computer Vision – ACCV 2006</BookTitle></PartContext></PartInfo><Chapter ID="Chap93" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>93</ChapterID><ChapterDOI>10.1007/11612704_93</ChapterDOI><ChapterSequenceNumber>93</ChapterSequenceNumber><ChapterTitle Language="En">High Quality Compression of Educational Videos Using Content-Adaptive Framework</ChapterTitle><ChapterFirstPage>933</ChapterFirstPage><ChapterLastPage>942</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2006</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>7</PartID><BookID>978-3-540-32432-4</BookID><BookTitle>Computer Vision – ACCV 2006</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>Ankush</GivenName><FamilyName>Mittal</FamilyName></AuthorName><Contact><Email>ankumfec@iitr.ernet.in</Email></Contact></Author><Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>Ankur</GivenName><FamilyName>Jain</FamilyName></AuthorName><Contact><Email>jainnuec@iitr.ernet.in</Email></Contact></Author><Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>Sourabh</GivenName><FamilyName>Jain</FamilyName></AuthorName><Contact><Email>soravuec@iitr.ernet.in</Email></Contact></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Sumit</GivenName><FamilyName>Gupta</FamilyName></AuthorName><Contact><Email>sumitfec@iitr.ernet.in</Email></Contact></Author><Affiliation ID="Aff19"><OrgDivision>Department of Computer Science and Enginnering</OrgDivision><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Roorkee</City><State>UA</State><Postcode>247667</Postcode><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff20"><OrgDivision>Department of Computer Science and Engg.</OrgDivision><OrgName>MVGR College of Engg.</OrgName><OrgAddress><City>Vizianagaram</City><State>A.P.</State><Postcode>535005</Postcode><Country>India</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>With multimedia E-learning becoming popular, better compression algorithms are required that are space efficient but maintain the quality of video data, particularly slide and blackboard text. In this paper, an educational video compression technique is presented that dynamically allocates the space according to the content importance of each video segment in the educational videos. We present a phase correlation based motion estimation and compensation algorithm to encode important moving objects in efficient manner. Temporal coherence is exploited in a two phase manner. First, the frames with high similarity are categorized and encoded efficiently. Secondly, the compression ratio is adapted according to the frame content. The algorithm is compared with the state-of-the-art standards such as H.261, MPEG-4, etc. on large database. The comparison shows that for similar bit rates, the video quality for our algorithm is significantly better than the other methods.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>High Quality Compression of Educational Videos Using Content-Adaptive Framework</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>High Quality Compression of Educational Videos Using Content-Adaptive Framework</title></titleInfo><name type="personal"><namePart type="given">Ankush</namePart><namePart type="family">Mittal</namePart><affiliation>Department of Computer Science and Enginnering, Indian Institute of Technology, 247667, Roorkee, UA, India</affiliation><affiliation>E-mail: ankumfec@iitr.ernet.in</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Ankur</namePart><namePart type="family">Jain</namePart><affiliation>Department of Computer Science and Enginnering, Indian Institute of Technology, 247667, Roorkee, UA, India</affiliation><affiliation>E-mail: jainnuec@iitr.ernet.in</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Sourabh</namePart><namePart type="family">Jain</namePart><affiliation>Department of Computer Science and Enginnering, Indian Institute of Technology, 247667, Roorkee, UA, India</affiliation><affiliation>E-mail: soravuec@iitr.ernet.in</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Sumit</namePart><namePart type="family">Gupta</namePart><affiliation>Department of Computer Science and Engg., MVGR College of Engg., 535005, Vizianagaram, A.P., India</affiliation><affiliation>E-mail: sumitfec@iitr.ernet.in</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2006</dateIssued><copyrightDate encoding="w3cdtf">2006</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: With multimedia E-learning becoming popular, better compression algorithms are required that are space efficient but maintain the quality of video data, particularly slide and blackboard text. In this paper, an educational video compression technique is presented that dynamically allocates the space according to the content importance of each video segment in the educational videos. We present a phase correlation based motion estimation and compensation algorithm to encode important moving objects in efficient manner. Temporal coherence is exploited in a two phase manner. First, the frames with high similarity are categorized and encoded efficiently. Secondly, the compression ratio is adapted according to the frame content. The algorithm is compared with the state-of-the-art standards such as H.261, MPEG-4, etc. on large database. The comparison shows that for similar bit rates, the video quality for our algorithm is significantly better than the other methods.</abstract><relatedItem type="host"><titleInfo><title>Computer Vision – ACCV 2006</title><subTitle>7th Asian Conference on Computer Vision, Hyderabad, India, January 13-16, 2006. 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