Change search
ReferencesLink to record
Permanent link

Direct link
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
URN: urn:nbn:se:bth-7478Local ID: diva2:835101
4th International Congress on Image and Signal Processing
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)28 downloads
File information
File name FULLTEXT01.pdfFile size 295 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Shahid, MuhammadLövström, Benny
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 28 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

Total: 30 hits
ReferencesLink to record
Permanent link

Direct link