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Volume: 32 | Article ID: art00007
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Analyzing the performance of autoencoder-based objective quality metrics on audio-visual content
  DOI :  10.2352/ISSN.2470-1173.2020.9.IQSP-167  Published OnlineJanuary 2020
Abstract

The development of audio-visual quality models faces a number of challenges, including the integration of audio and video sensory channels and the modeling of their interaction characteristics. Commonly, objective quality metrics estimate the quality of a single component (audio or video) of the content. Machine learning techniques, such as autoencoders, offer as a very promising alternative to develop objective assessment models. This paper studies the performance of a group of autoencoder-based objective quality metrics on a diverse set of audio-visual content. To perform this test, we use a large dataset of audio-visual content (The UnB-AV database), which contains degradations in both audio and video components. The database has accompanying subjective scores collected on three separate subjective experiments. We compare our autoencoder-based methods, which take into account both audio and video components (multi-modal), against several objective (single-modal) audio and video quality metrics. The main goal of this work is to verify the gain or loss in performance of these single-modal metrics, when tested on audio-visual sequences.

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Helard Becerra Martinez, Mylène C.Q. Farias, Andrew Hines, "Analyzing the performance of autoencoder-based objective quality metrics on audio-visual contentin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XVII,  2020,  pp 167-1 - 167-6,  https://doi.org/10.2352/ISSN.2470-1173.2020.9.IQSP-167

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