A Reduced Complexity No-Reference Artificial Neural Network Based Video Quality Predictor

Document type: Conference Papers
Peer reviewed: Yes
Full text:
Author(s): Muhammad Shahid, Andreas Rossholm, Benny Lövström
Title: A Reduced Complexity No-Reference Artificial Neural Network Based Video Quality Predictor
Conference name: 4th International Congress on Image and Signal Processing
Year: 2011
Publisher: IEEE
City: Shanghai, China
Organization: Blekinge Institute of Technology
Department: School of Engineering - Dept. of Electrical Engineering (Sektionen för ingenjörsvetenskap - Avd. för elektroteknik)
School of Engineering S-371 79 Karlskrona
+46 455 38 50 00
http://www.bth.se/ing/
Authors e-mail: muhammad.shahid@ieee.org, a.rossholm@gmail.com, blo@bth.se
Language: English
Abstract: 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.
Subject: Signal processing\Image and Video Processing
Signal Processing\General
Note: The paper has been accepted for presentation in the conference.
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