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Real-time video quality analysis on mobile devices

Authors:

Werner BailerAuthors Info & Claims

MoVid '15: Proceedings of the 7th ACM International Workshop on Mobile Video

Pages 23 - 24

https://doi.org/10.1145/2727040.2727041

Published: 18 March 2015 Publication History

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Abstract

Media capture of live events such as concerts can be improved by including user generated content. In this paper we propose methods for visual quality analysis on mobile devices, in order to provide direct feedback to the contributing user about the quality of the captured content and avoid wasting bandwidth and battery for uploading/streaming low-quality content. We focus on quality analysis that complements information we can obtain from other sensors. The proposed methods include real-time algorithms for sharpness, noise and over-/underexposure.

References

[1]

R. Hassen, Z. Wang, and M. Salama. No-reference image sharpness assessment based on local phase coherence measurement. In Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, pages 2434--2437, March 2010.

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[2]

H. Sheikh, Z. Wang, L. Cormack, and A. Bovik. LIVE image quality assessment database, release 2 {Online}. http://live.ece.utexas.edu/research/quality. Accessed: Dec. 2014.

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Cited By

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Ephrath JEliasof MRuthotto LHaber ETreister E(2020)LeanConvNets: Low-Cost Yet Effective Convolutional Neural NetworksIEEE Journal of Selected Topics in Signal Processing10.1109/JSTSP.2020.297277514:4(894-904)Online publication date: May-2020
https://doi.org/10.1109/JSTSP.2020.2972775
Bailer WWeiss WWechtitsch S(2016)Selecting User Generated Content for Use in Media ProductionsMultiMedia Modeling10.1007/978-3-319-27674-8_38(388-393)Online publication date: 1-Jan-2016
https://doi.org/10.1007/978-3-319-27674-8_38

Index Terms

Real-time video quality analysis on mobile devices
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
2. Information systems
  1. Information systems applications
    1. Multimedia information systems

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Published In

MoVid '15: Proceedings of the 7th ACM International Workshop on Mobile Video

March 2015

38 pages

ISBN:9781450333535

DOI:10.1145/2727040

Conference Chairs:
Pål Halvorsen
University of Oslo/Simula Research Lab, Norway
,
Nikil Dutt
University of California Irvine

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 March 2015

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Short-paper

Funding Sources

European Union's Seventh Framework Programme

Conference

MMSys '15

Sponsor:

MMSys '15: Multimedia Systems Conference 2015

March 18 - 20, 2015

Oregon, Portland

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Cited By

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Ephrath JEliasof MRuthotto LHaber ETreister E(2020)LeanConvNets: Low-Cost Yet Effective Convolutional Neural NetworksIEEE Journal of Selected Topics in Signal Processing10.1109/JSTSP.2020.297277514:4(894-904)Online publication date: May-2020
https://doi.org/10.1109/JSTSP.2020.2972775
Bailer WWeiss WWechtitsch S(2016)Selecting User Generated Content for Use in Media ProductionsMultiMedia Modeling10.1007/978-3-319-27674-8_38(388-393)Online publication date: 1-Jan-2016
https://doi.org/10.1007/978-3-319-27674-8_38

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Content and Metadata Workflow for User Generated Content in Live Production