Analyzing the performance of autoencoder-based objective quality metrics on audio-visual content

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Title: Analyzing the performance of autoencoder-based objective quality metrics on audio-visual content
Authors: Martinez, HelardFarias, Mylène C.Q.Hines, Andrew
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Date: 30-Jan-2020
Online since: 2020-04-29T14:57:27Z
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.
Funding Details: Science Foundation Ireland
metadata.dc.description.othersponsorship: 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
Keywords: Audio qualityVideo qualityAutoencoderNo-reference quality metricAudio degradationsVideo degradations
DOI: 10.2352/ISSN.2470-1173.2020.9.IQSP-167
Language: en
Status of Item: Peer reviewed
Conference Details: The 2020 IS&T International Symposium on Electronic Imaging (EI2020), Burlingame, California, 26-30 January 2020
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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