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The Konstanz Natural Video Database (KoNViD-1k)

Hosu, V, Hahn, F, Jenadeleh, M, Lin, H, Men, H, Sziranyi, T, Li, Shujun and Saupe, D (2017) The Konstanz Natural Video Database (KoNViD-1k) In: 9th International Conference on Quality of Multimedia Experience (QoMEX 2017), 2017-05-31 - 2017-06-02, Erfurt, Germany.

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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 (Conference Paper)
Subjects : Computing Science
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Hosu, VUNSPECIFIEDUNSPECIFIED
Hahn, FUNSPECIFIEDUNSPECIFIED
Jenadeleh, MUNSPECIFIEDUNSPECIFIED
Lin, HUNSPECIFIEDUNSPECIFIED
Men, HUNSPECIFIEDUNSPECIFIED
Sziranyi, TUNSPECIFIEDUNSPECIFIED
Li, Shujunshujun.li@surrey.ac.ukUNSPECIFIED
Saupe, DUNSPECIFIEDUNSPECIFIED
Date : 2017
Copyright Disclaimer : © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Contributors :
ContributionNameEmailORCID
UNSPECIFIEDIEEE, UNSPECIFIEDUNSPECIFIED
Uncontrolled Keywords : Video database; authentic video; video quality assessment; fair sampling; crowdsourcing.
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 02 May 2017 08:31
Last Modified : 04 Jul 2017 15:13
URI: http://epubs.surrey.ac.uk/id/eprint/814067

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