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Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing. Ericsson AB. Åbo Akademi University.. (Visual Technologies.)
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The demand of digital video with higher resolution is increasing everyday and in amodern world the videos are consumed in all kinds of multimedia devices. The transmissionof higher quality videos over the internet require higher bandwidth, which isnot an acceptable option. So, it is necessary to compress the video to a compact file byremoving redundancies and detail information.

The process of compressing a video file requires a lot of complex calculations,which is a time consuming process, specially for live telecasting or real-time videoconferencing. In addition videos with higher quality such as higher number of Frameper Second (FPS) or higher resolution like HD and 4k video requires huge redundantdata processing. Hence, this operation causes delays during the video playback. Tominimize the time delay for the video coding, there are coding methods such as losslessand lossy coding which has been around for a long time. However, the idea to increasethe number of processing unit like CPUs and memory for video coding software is anarea that require further research to improve coding techniques.

Distributed system uses available resources in the network to achieve a commongoal. It explores the available infrastructure so that the task can be done in parallel. Cloud computing is a great example of distributed system which has fully dedicatedresources for such complex jobs.

This thesis deals with these areas in real-time to lower the video coding delaythrough investigating distributed resources as well as the parallelization in video codingstandards such as AVC and HEVC. It has been carried out with a collaborationwith Ericsson Research in Stockholm.

Place, publisher, year, edition, pages
2016. , 73 p.
Blekinge Tekniska Högskola Forskningsrapport, ISSN 1103-1581
Keyword [en]
Distributed Transcoding, Distributed Computing, Apache storm, Scheduling, Openstack, Cloud Computing
National Category
Signal Processing Embedded Systems
URN: urn:nbn:se:bth-11911OAI: diva2:930908
External cooperation
Ericsson AB, Åbo Akademi.
Subject / course
ET2524 Master's Thesis (120 credits) in Electrical Engineering with emphasis on Signal Processing
Educational program
ETASB Master of Science Programme in Electrical Engineering with emphasis on Signal Processing
2016-03-18, SE KI 30 05 5234 Lustgarden, FÄRÖGATAN 6, STOCKHOLM, 09:30 (English)
Available from: 2016-06-08 Created: 2016-05-25 Last updated: 2016-06-08Bibliographically approved

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