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How deep is your encoder: an analysis of features descriptors for an autoencoder-based audio-visual quality metric
The development of audio-visual quality assessment models poses a number of challenges in order to obtain accurate predictions.
NAViDAd: A No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder
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.
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