Master data

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

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
Type: Registered lecture
Homepage: https://www.2021.ieeeicip.org/
Event: IEEE International Conference on Image Processing (IEEE ICIP 2021) (Anchorage, Alaska)
Date: 21.09.2021
lecture status: stattgefunden (online)

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
Focus of lecture
  • Science to Science (Quality indicator: II)
Classification raster of the assigned organisational units:
Group of participants
  • Mainly international
Published?
  • Yes
working groups
  • Multimedia Communication

Cooperations

No partner organisations selected