Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Objective Video Quality Assessment - Towards large scale video database enhanced model development
University of Nantes, France.
Polytechnic University of Turin, Italy.
Ghent University, Belgium.
RISE, Swedish ICT, Acreo. Mid Sweden University, Sweden.ORCID iD: 0000-0001-5060-9402
Show others and affiliations
2015 (English)In: IEICE transactions on communications, ISSN 0916-8516, E-ISSN 1745-1345, Vol. E98.B, no 1, p. 2-11Article in journal (Refereed) Published
Abstract [en]

The current development of video quality assessment algorithms suffers from the lack of available video sequences for training, verification and validation to determine and enhance the algorithm’s application scope. The Joint Effort Group of the Video Quality Experts Group (VQEG-JEG) is currently driving efforts towards the creation of large scale, reproducible, and easy to use databases. These databases will contain bitstreams of recent video encoders (H.264, H.265), packet loss impairment patterns and impaired bitstreams, pre-parsed bitstream information into files in XML syntax, and well-known objective video quality measurement outputs. The database is continuously updated and enlarged using reproducible processing chains. Currently, more than 70,000 sequences are available for statistical analysis of video quality measurement algorithms. New research questions are posed as the database is designed to verify and validate models on a very large scale, testing and validating various scopes of applications, while subjective assessment has to be limited to a comparably small subset of the database. Special focus is given on the principles guiding the database development, and some results are given to illustrate the practical usefulness of such a database with respect to the detailed new research questions.

Place, publisher, year, edition, pages
2015. Vol. E98.B, no 1, p. 2-11
Keywords [en]
video quality assessment, large scale database, reproducible research
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-31917DOI: 10.1587/transcom.E98.B.2Scopus ID: 2-s2.0-84924690334OAI: oai:DiVA.org:ri-31917DiVA, id: diva2:1151775
Available from: 2017-10-24 Created: 2017-10-24 Last updated: 2023-05-25Bibliographically approved

Open Access in DiVA

fulltext(2035 kB)145 downloads
File information
File name FULLTEXT01.pdfFile size 2035 kBChecksum SHA-512
b8d640a59dd2c36020a85ec2aa5e0ba9b6af6913ca9ef05b38db6a66ba8d7431e1b7022756af73a778469fc9ae2ecba1c3ef483e86b5fa26afa2f9808fab4b17
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Brunnström, Kjell

Search in DiVA

By author/editor
Brunnström, Kjell
By organisation
Acreo
In the same journal
IEICE transactions on communications
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 145 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

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 192 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf