Analyzing the performance of autoencoder-based objective quality metrics on audio-visual content
|Title:||Analyzing the performance of autoencoder-based objective quality metrics on audio-visual content||Authors:||Martinez, Helard; Farias, Mylène C.Q.; Hines, Andrew||Permanent link:||http://hdl.handle.net/10197/11353||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 quality; Video quality; Autoencoder; No-reference quality metric; Audio degradations; Video 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|
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