Change search
ReferencesLink to record
Permanent link

Direct link
Model-Based Video Coding Using a Colour and Depth Camera
Linköping University, Department of Electrical Engineering, Computer Vision.
2011 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Modellbaserad videokodning med hjälp av färg- och djupkamera (Swedish)
Abstract [en]

In this master thesis, a model-based video coding algorithm has been developed that uses input from a colour and depth camera, such as the Microsoft Kinect. Using a model-based representation of a video has several advantages over the commonly used block-based approach, used by the H.264 standard. For example, videos can be rendered in 3D, be viewed from alternative views, and have objects inserted into them for augmented reality and user interaction.

This master thesis demonstrates a very efficient way of encoding the geometry of a scene. The results of the proposed algorithm show that it can reach very low bitrates with comparable results to the H.264 standard.

Abstract [sv]

I detta examensarbete har en modellbaserad videokodningsalgoritm utvecklats som använder data från en djup- och färgkamera, exempelvis Microsoft Kinect. Det finns flera fördelar med en modellbaserad representation av en video över den mer vanligt förekommande blockbaserade varianten, vilket används av bland annat H.264. Några exempel är möjligheten att rendera videon i 3D samt från alternativa vyer, placera in objekt i videon samt möjlighet för användaren att interagera med scenen.

Detta examensarbete påvisar en väldigt effektiv metod för komprimering av scengeometri. Resultaten av den presenterade algoritmen visar att möjligheten att uppnå väldigt låg bithastighet med jämförelsebara resultat med H.264-standarden.

Place, publisher, year, edition, pages
2011. , 54 p.
Keyword [en]
Model-based video coding, Colour and depth camera, Geometry compression
National Category
Computer Vision and Robotics (Autonomous Systems) Signal Processing Information Science Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-68737ISRN: LiTH-ISY-EX--11/4463--SEOAI: oai:DiVA.org:liu-68737DiVA: diva2:420400
Subject / course
Information Coding
Uppsok
Technology
Supervisors
Examiners
Available from: 2011-06-01 Created: 2011-06-01 Last updated: 2011-06-01Bibliographically approved

Open Access in DiVA

fulltext(8196 kB)462 downloads
File information
File name FULLTEXT01.pdfFile size 8196 kBChecksum SHA-512
509cd141c40ec4f2138bf0906b0241dd86a0755230e9edf3980534992d73fc39616184038936c1868626872067b6cdd8d1831a3c6ae55602ada51b0f82cdb564
Type fulltextMimetype application/pdf

By organisation
Computer Vision
Computer Vision and Robotics (Autonomous Systems)Signal ProcessingInformation ScienceComputer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 462 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 334 hits
ReferencesLink to record
Permanent link

Direct link