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Analyzing the performance of autoencoder-based objective quality metrics on audio-visual content
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File | Description | Size | Format | |
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insight_publication.pdf | 2.95 MB |
Date Issued
30 January 2020
Date Available
29T14:57:27Z April 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.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
Type of Material
Conference Publication
Publisher
Society for Imaging Science and Technology
Copyright (Published Version)
2020 Society for Imaging Science and Technology
Language
English
Status of Item
Peer reviewed
Description
The 2020 IS&T International Symposium on Electronic Imaging (EI2020), Burlingame, California, 26-30 January 2020
ISSN
2470-1173
This item is made available under a Creative Commons License
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