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. © 2009 Springer Berlin Heidelberg.