Paper Info
Title
Feature Selection for Neural-Network Based No-Reference Video Quality Assessment
Abstract
Design of algorithms that are able to estimate video quality as perceived by human observers is of interest for a number of applications. Depending on the video content, the artifacts introduced by the coding process can be more or less pronounced and diversely affect the quality of videos, as estimated by humans. In this paper we propose a new scheme for quality assessment of coded video streams, based on suitably chosen set of objective quality measures driven by human perception. Specifically, the relation of large number of objective measure features related to video coding artifacts is examined. Standardized procedure has been used to calculate the Mean Opinion Score (MOS), based on experiments conducted with a group of non-expert observers viewing SD sequences. MOS measurements were taken for nine different standard definition (SD) sequences, coded using MPEG-2 at five different bit-rates. Eighteen different published approaches for measuring the amount of coding artifacts objectively were implemented. The results obtained were used to design a novel no-reference MOS estimation algorithm using a multi-layer perceptron neural-network.
Year
DOI
Venue
2009
10.1007/978-3-642-04277-5_64
ICANN
Keywords
Field
DocType
video quality assessment,no-reference approach,perceptual quality,neural-networks,multi-layer perceptron.
Data mining,Pattern recognition,Feature selection,Computer science,Coding (social sciences),Subjective video quality,Mean opinion score,Multilayer perceptron,Artificial intelligence,Artificial neural network,Perceptron,Video quality
Conference
Volume
ISSN
Citations 
5769
0302-9743
5
PageRank 
References 
Authors
0.48
7
5
Name
Order
Citations
PageRank
Dubravko Culibrk127920.02
Dragan Kukolj218417.11
Petar Vasiljević350.48
Maja Pokric4675.07
Vladimir Zlokolica516919.87