  The development of models for quality prediction of both audio and video
signals is a fairly mature field. But, although several multimodal models have
been proposed, the area of audio-visual quality prediction is still an emerging
area. In fact, despite the reasonable performance obtained by combination and
parametric metrics, currently there is no reliable pixel-based audio-visual
quality metric. The approach presented in this work is based on the assumption
that autoencoders, fed with descriptive audio and video features, might produce
a set of features that is able to describe the complex audio and video
interactions. Based on this hypothesis, we propose a No-Reference Audio-Visual
Quality Metric Based on a Deep Autoencoder (NAViDAd). The model visual features
are natural scene statistics (NSS) and spatial-temporal measures of the video
component. Meanwhile, the audio features are obtained by computing the
spectrogram representation of the audio component. The model is formed by a
2-layer framework that includes a deep autoencoder layer and a classification
layer. These two layers are stacked and trained to build the deep neural
network model. The model is trained and tested using a large set of stimuli,
containing representative audio and video artifacts. The model performed well
when tested against the UnB-AV and the LiveNetflix-II databases. %Results shows
that this type of approach produces quality scores that are highly correlated
to subjective quality scores.
