A Reduced Complexity No-Reference Artificial Neural Network Based Video Quality Predictor
Blekinge Institute of Technology, School of Engineering, Department of Electrical Engineering2011 (English)Conference paper (Refereed) Published
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.
IdentifiersURN: urn:nbn:se:bth-7478Local ID: oai:bth.se:forskinfo846110F97479B9FAC12578ED003465B8OAI: oai:DiVA.org:bth-7478DiVA: diva2:835101
4th International Congress on Image and Signal Processing
The paper has been accepted for presentation in the conference.2012-09-182011-08-152015-06-30Bibliographically approved