# NAViDAd: A No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder

arXiv: https://arxiv.org/abs/2001.11406 · HTML: https://mlsanity.com/paper/2001.11406/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech](https://mlsanity.com/paper/2402.16321/) (2024)
- [Telepresence Video Quality Assessment](https://mlsanity.com/paper/2207.09956/) (2022)
- [Network Bending of Diffusion Models for Audio-Visual Generation](https://mlsanity.com/paper/2406.19589/) (2024)
- [Multimodal Confidence Modeling in Audio-Visual Quality Assessment](https://mlsanity.com/paper/2605.01219/) (2026)

## Abstract (arXiv)

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.