  The 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's
architecture and eventually develop a much better audio-visual quality metric.
