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 Video transcoding using machine learning.


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Type of Resource: text

Genre: Electronic Thesis or Dissertation

Date Issued: 2008

Publisher: Florida Atlantic University

Physical Form: electronic

Extent: ix, 48 p. : ill. (some col.).

Language(s): English

Summary: The field of Video Transcoding has been evolving throughout the past ten years. The need for transcoding of video files has greatly increased because of the new upcoming standards which are incompatible with old ones. This thesis takes the method of using machine learning for video transcoding mode decisions and discusses ways to improve the process of generating the algorithm for implementation in different video transcoders. The transcoding methods used decrease the complexity in the mode decision inside the video encoder. Also methods which automate and improve results are discussed and implemented in two different sets of transcoders: H.263 to VP6 , and MPEG-2 to H.264. Both of these transcoders have shown a complexity loss of almost 50%. Video transcoding is important because the quantity of video standards have been increasing while devices usually can only decode one specific codec.

Identifier: 316795951 (oclc), 166451 (digitool), FADT166451 (IID), fau:2834 (fedora)

Note(s): by Christopher Holder.Thesis (M.S.C.S.)--Florida Atlantic University, 2008.Includes bibliography.Electronic reproduction. Boca Raton, Fla., 2008. Mode of access: World Wide Web.

Subject(s): Coding theoryImage transmission -- Technological innovationsFile conversion (Computer science)Data structures (Computer science)MPEG (Video coding standard)Digital mediaVideo compression

Persistent Link to This Record: http://purl.flvc.org/FAU/166451

Owner Institution: FAU



Autor: Holder, Christopher.

Fuente: http://fau.digital.flvc.org/islandora/object/fau%3A2834



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