UnB-AV: An Audio-Visual Database for Multimedia Quality Research

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Title: UnB-AV: An Audio-Visual Database for Multimedia Quality Research
Authors: Martinez, HelardHines, AndrewFarias, Mylène C.Q.
Permanent link: http://hdl.handle.net/10197/11936
Date: 19-Mar-2020
Online since: 2021-02-09T10:47:08Z
Abstract: In this paper we present the UnB-AV database, which is a database of audio-visual sequences and quality scores aimed at multimedia quality research. The database contains a total of 140 source content, with a diverse semantic content, both in terms of the video and audio components. It also contains 2,320 test sequences with audio and video degradations, along with the corresponding quality and content subjective scores. The subjective scores were collected by performing 3 different psycho-physical experiments using the Immersive Methodology. The three experiments have been presented individually in previous studies. In the first experiment, only the video component of the audio-visual sequences were degraded with compression (H.264 and H.265) and transmission (packet-loss and frame freezing) distortions. In the second experiment, only the audio component of the audio-visual sequences were degraded with common audio distortions (clip, echo, chop, and background noise). Finally, in the third experiment the audio and video degradations were combined to degrade both audio and video components. The UnB-AV database is available for download from the site of the Laboratory of Digital Signal Processing of the University of Brasilia and The Consumer Digital Video Library (CDVL).
Funding Details: European Commission - European Regional Development Fund
Science Foundation Ireland
Funding Details: Insight Research Centre
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES
Fundação de Apoio à Pesquisa do Distrito Federa l(FAPDF)
University of Brasília (UnB)
Type of material: Journal Article
Publisher: IEEE
Journal: IEEE Access
Volume: 8
Start page: 56641
End page: 56649
Keywords: Machine learning & statisticsStreaming mediaDegradationMultimedia databasesBit rateQuality assessment
DOI: 10.1109/ACCESS.2020.2981861
Language: en
Status of Item: Peer reviewed
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by/3.0/ie/
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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