How Deep is Your Encoder: An Analysis of Features Descriptors for an Autoencoder-Based Audio-Visual Quality Metric

Abstract

International Conference on Quality of Multimedia Experience (QoMEX), Athlone, Ireland (held online due to coronavirus outbreak), 26-28 May 2020The development of audio-visual quality assessment models poses a number of challenges in order to obtain accurate predictions. One of these challenges is the modelling of the complex interaction that audio and visual stimuli have and how this interaction is interpreted by human users. The No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder (NAViDAd) deals with this problem from a machine learning perspective. The metric receives two sets of audio and video features descriptors and produces a low-dimensional set of features used to predict the audio-visual quality. A basic implementation of NAViDAd was able to produce accurate predictions tested with a range of different audio-visual databases. The current work performs an ablation study on the base architecture of the metric. Several modules are removed or re-trained using different configurations to have a better understanding of the metric functionality. The results presented in this study provided important feedback that allows us to understand the real capacity of the metric\u27s architecture and eventually develop a much better audio-visual quality metric.Science Foundation IrelandInsight Research CentreConselho Nacional de Desenvolvimento Cientfico eTecnol ogico (CNPq)Coordenacao de Aperfeicoamento de Pessoal de Nıvel Superior (CAPES)Fundacao de Apoio aPesquisa do Distrito Federal (FAPDF)University of Brasılia (UnB)2020-12-15 JG: external PDF cover page remove