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A Reduced Complexity No-Reference Artificial Neural Network Based Video Quality Predictor
Responsible organisation
2011 (English)Conference paper, (Refereed) Published
Abstract [en]

There is a growing need for robust methods for reference free perceptual quality measurements due to the increasing use of video in hand-held multimedia devices. These methods are supposed to consider pertinent artifacts introduced by the compression algorithm selected for source coding. This paper proposes a model that uses readily available encoder parameters as input to an artificial neural network to predict objective quality metrics for compressed video without using any reference and without need for decoding. The results verify its robustness for prediction of objective quality metrics in general and for PEVQ and PSNR in particular. The paper also focuses on reducing the complexity of the neural network.

Place, publisher, year, edition, pages
Shanghai, China: IEEE , 2011.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-7478Local ID: oai:bth.se:forskinfo846110F97479B9FAC12578ED003465B8OAI: oai:DiVA.org:bth-7478DiVA: diva2:835101
Conference
4th International Congress on Image and Signal Processing
Note
The paper has been accepted for presentation in the conference.Available from: 2012-09-18 Created: 2011-08-15 Last updated: 2015-06-30Bibliographically approved

Open Access in DiVA

fulltext(295 kB)51 downloads
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Shahid, MuhammadLövström, Benny
Signal Processing

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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