Master data

Title: Efficient Content-Adaptive Feature-Based Shot Detection for HTTP Adaptive Streaming
Subtitle:
Abstract:

Video delivery over the Internet has been becoming a commodity in recent years, owing to the widespread use of Dynamic Adaptive Streaming over HTTP (DASH). The DASH specification defines a hierarchical data model for Media Presentation Descriptions (MPDs) in terms of segments. This paper focuses on segmenting video into multiple shots for encoding in Video on Demand (VoD) HTTP Adaptive Streaming (HAS) applications. Therefore, we propose a novel Discrete Cosine Transform (DCT) feature-based shot detection and successive elimination algorithm for shot detection and compare it against the default shot detection algorithm of the x265 implementation of the High Efficiency Video Coding (HEVC) standard. Our experimental results demonstrate that our proposed feature-based pre-processor has a recall rate of 25% and an F-measure of 20% greater than the benchmark algorithm for shot detection.

Keywords: HTTP Adaptive Streaming, Video-on-Demand, Shot detection, multi-shot encoding
Publication type: Article in Proceedings (Authorship)
Publication date: 23.08.2021 (Print)
Published by: ICIP '21 Proceedings of the IEEE International Conference on Image Processing
ICIP '21 Proceedings of the IEEE International Conference on Image Processing
to publication
 ( IEEE; )
Title of the series: -
Volume number: -
First publication: Yes
Version: -
Page: pp. 2174 - 2178

Versionen

Keine Version vorhanden
Publication date:
ISBN (e-book): -
eISSN: -
DOI: http://dx.doi.org/10.1109/icip42928.2021.9506092
Homepage: -
Open access
  • Available online (open access)
Publication date: 23.08.2021
ISBN:
  • 978-1-6654-4115-5
  • 978-1-6654-3102-6
ISSN: 2381-8549
Homepage: https://ieeexplore.ieee.org/document/9506092

Assignment

Organisation Address
Fakultät für Technische Wissenschaften
 
Institut für Informationstechnologie
Universitaetsstr. 65-67
9020 Klagenfurt am Wörthersee
Austria
   martina.steinbacher@aau.at
http://itec.aau.at/
To organisation
Universitaetsstr. 65-67
AT - 9020  Klagenfurt am Wörthersee

Categorisation

Subject areas
  • 1020 - Computer Sciences
Research Cluster No research Research Cluster selected
Peer reviewed
  • Yes
Publication focus
  • Science to Science (Quality indicator: II)
Classification raster of the assigned organisational units:
working groups
  • Multimedia Communication

Cooperations

No partner organisations selected

Articles of the publication

No related publications