Automated File-Based Quality Control: A Machine-Learning Approach

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Publisher: SMPTE — White Plains, NY
Doc Type: Conference Paper
Content Type: Original Research
Volume: 2011, No. 1, pp. 1–6
Abstract: In recent years, broadcasters successfully introduced file-based workflows to improve production efficiency. However, they are increasingly dealing with a proliferation of file formats, and many of them still have large archives that need to be digitized for reuse. To guarantee trouble-free workflows and long-term preservation in this quickly evolving digital domain, it is essential that media files adhere to well-described, established standards. Furthermore, their audiovisual quality should be up to broadcast level. A variety of content analysis tools checking container and encoding formats, as well as audiovisual quality, are available but often hard to configure, and frequently provide difficult-to-interpret results. In this research, a learning algorithm takes into account the results of several sources of content analysis to perform a reliable automatic interpretation, which is communicated as a traffic light decision to an operator who can then take further action if necessary. Thus, valuable time and money can be saved.
Publication Date: 2011-10-01
DOI: 10.5594/M001058
Link: https://doi.org/10.5594/M001058
Author(s): Matthias De GeyterVRT-medialab, Gaston Crommenlaan 10 (Bus 101), B-9050 Ghent
Nick VercammenIBCN, Ghent University — IBBT, Gaston Crommenlaan 8 (Bus 201), B-9050 Ghent
Dirk DeschrijverIBCN, Ghent University — IBBT, Gaston Crommenlaan 8 (Bus 201), B-9050 Ghent
Tom DhaeneIBCN, Ghent University — IBBT, Gaston Crommenlaan 8 (Bus 201), B-9050 Ghent
Piet DemeesterIBCN, Ghent University — IBBT, Gaston Crommenlaan 8 (Bus 201), B-9050 Ghent
Brecht VermeulenIBCN, Ghent University — IBBT, Gaston Crommenlaan 8 (Bus 201), B-9050 Ghent
Keyword(s): File-Based Workflow, Automation
Copyright: © 2011 Society of Motion Picture and Television Engineers, Inc.

Bibliographic Reference(s)

1. De Geyter M. Overmeire L. , “File-Based Workflows: Key Challenges in Real-World Facilities” SMPTE Motion & Imaging Journal , 37 – 42 , March 2011 . EXTERNAL

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Matthias De Geyter, Nick Vercammen, Dirk Deschrijver, Tom Dhaene, Piet Demeester, and Brecht Vermeulen; Automated File-Based Quality Control: A Machine-Learning Approach, SMPTE Meetings and Conferences ( October 2011); SMPTE, 2011. Available at https://doi.org/10.5594/M001058

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Matthias De Geyter, Nick Vercammen, Dirk Deschrijver, Tom Dhaene, Piet Demeester, and Brecht Vermeulen; Automated File-Based Quality Control: A Machine-Learning Approach, SMPTE Meetings and Conferences ( October 2011); SMPTE, 2011. Available at https://doi.org/10.5594/M001058

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<span class="citation">Matthias De Geyter, Nick Vercammen, Dirk Deschrijver, Tom Dhaene, Piet Demeester, and Brecht Vermeulen; <cite>Automated File-Based Quality Control: A Machine-Learning Approach</cite>, SMPTE Meetings and Conferences ( October 2011); SMPTE, 2011. Available at <a href="https://doi.org/10.5594/M001058" target="_blank" rel="noopener">https://doi.org/10.5594/M001058</a></span>

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Matthias De Geyter, Nick Vercammen, Dirk Deschrijver, Tom Dhaene, Piet Demeester, and Brecht Vermeulen; <cite id="bib-10-5594-m001058">Automated File-Based Quality Control: A Machine-Learning Approach</cite>, SMPTE Meetings and Conferences ( October 2011); SMPTE, 2011
<span class="doi">10.5594/M001058</span>
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