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Jae S. Lim. Heng, Brian A., 1977- Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2005-08-23T21:22:28Z 2005-08-23T21:22:28Z 2001 2001 http://hdl.handle.net/1721.1/8572 49223382 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001. Includes bibliographical references (p. 91-93). As the cost of video technology has fallen, surveillance cameras have become an integral part of a vast number of security systems. However, even with the introduction of progressive video displays, the majority of these systems still use interlaced scanning so that they may be connected to standard television monitors. When law enforcement officials analyze surveillance video, they are often interested in carefully examining a few frames of interest. However, it is impossible to perform frame-by-frame analysis of interlaced surveillance video without performing interlaced to progressive conversion, also known as deinterlacing. In most surveillance systems, very basic techniques are used for deinterlacing, resulting in a number of severe visual artifacts and greatly limiting the intelligibility of surveillance video. This thesis investigates fourteen deinterlacing algorithms to determine methods that will improve the quality and intelligibility of video sequences acquired by surveillance systems. The advantages and disadvantages of each algorithm are discussed followed by both qualitative and quantitative comparisons. Motion adaptive deinterlacing methods are shown to have the most potential for surveillance video, demonstrating the highest performance both visually and in terms of peak signal-to-noise ratio. by Brian A. HEng. S.M. 93 p. 9476615 bytes 9476375 bytes application/pdf application/pdf eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Application of deinterlacing for the enhancement of surveillance video Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="a6df47ce-2380-44fe-b19d-4fb8730f9df8"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Application of deinterlacing for the enhancement of surveillance video</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2001</PublicationDate> <Authors> <Author> <DisplayName>Heng, Brian A., 1977-</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>As the cost of video technology has fallen, surveillance cameras have become an integral part of a vast number of security systems. However, even with the introduction of progressive video displays, the majority of these systems still use interlaced scanning so that they may be connected to standard television monitors. When law enforcement officials analyze surveillance video, they are often interested in carefully examining a few frames of interest. However, it is impossible to perform frame-by-frame analysis of interlaced surveillance video without performing interlaced to progressive conversion, also known as deinterlacing. In most surveillance systems, very basic techniques are used for deinterlacing, resulting in a number of severe visual artifacts and greatly limiting the intelligibility of surveillance video. This thesis investigates fourteen deinterlacing algorithms to determine methods that will improve the quality and intelligibility of video sequences acquired by surveillance systems. The advantages and disadvantages of each algorithm are discussed followed by both qualitative and quantitative comparisons. Motion adaptive deinterlacing methods are shown to have the most potential for surveillance video, demonstrating the highest performance both visually and in terms of peak signal-to-noise ratio.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>