Using overlapping subjective datasets to assess the performance of objective quality metrics on Scalable Video Coding and error concealment

Abstract

In this paper, four subjective video datasets are presented. The considered application is Scalable Video Coding used as an error-concealment mechanism. The presented datasets explore the relations between encoding parameters and perceived quality, under different network-impairment patterns and involve error-concealment on the decoder's side, to simulate a complete distribution channel. The datasets share a part of common configurations which enables, in the first part of the paper, to compare the outcomes from several Single Stimulus experiments and draw interesting correspondances between different types of distortion. In the second part of the paper, we analyse the performance of three common objective quality metrics on each step of the distribution channel, to identify the possible directions to be followed in order to improve their accuracy in predicting the perceived quality.