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The Konstanz Natural Video Database (KoNViD-1k)

Hosu, Vlad, Hahn, Franz, Jenadeleh, Mohsen, Lin, Hanhe, Men, Hui, Szirányi, Tamás, Li, Shujun, Saupe, Dietmar (2017) The Konstanz Natural Video Database (KoNViD-1k). In: 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX). Proceedings of the 2017 9th International Conference on Quality of Multimedia Experience. . IEEE, Germany ISBN 978-1-5386-4025-8. E-ISBN 978-1-5386-4024-1. (doi:10.1109/QoMEX.2017.7965673)

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https://doi.org/10.1109/QoMEX.2017.7965673

Abstract

Subjective video quality assessment (VQA) strongly depends on semantics, context, and the types of visual distortions. Currently, all existing VQA databases include only a small number of video sequences with artificial distortions. The development and evaluation of objective quality assessment methods would benefit from having larger datasets of real-world video sequences with corresponding subjective mean opinion scores (MOS), in particular for deep learning purposes. In addition, the training and validation of any VQA method intended to be ‘general purpose’ requires a large dataset of video sequences that are representative of the whole spectrum of available video content and all types of distortions. We report our work on KoNViD-1k, a subjectively annotated VQA database consisting of 1,200 publicdomain video sequences, fairly sampled from a large public video dataset, YFCC100m. We present the challenges and choices we have made in creating such a database aimed at ‘in the wild’ authentic distortions, depicting a wide variety of content.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/QoMEX.2017.7965673
Uncontrolled keywords: Video database; authentic video; video quality assessment; fair sampling; crowdsourcing
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.575 Multimedia systems
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics (see also: telecommunications) > TK7880 Applications of electronics (inc industrial & domestic) > TK7885 Computer engineering
Divisions: Faculties > University wide - Teaching/Research Groups > Centre for Cyber Security Research
Faculties > Sciences > School of Computing > Security Group
Depositing User: Shujun Li
Date Deposited: 16 Oct 2018 22:47 UTC
Last Modified: 09 Aug 2019 11:09 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/69606 (The current URI for this page, for reference purposes)
Li, Shujun: https://orcid.org/0000-0001-5628-7328
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