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Training a Deep Remastering Model

Published:24 July 2022Publication History

ABSTRACT

The success of video streaming platforms has pushed studios to make available TV shows from legacy catalog, and there is an increased demand for remastering this content. Ideally, film reels are re-scanned with modern devices directly into high quality digital format. However this is not always possible as parts of the original film reels can be damaged or missing, and the content is then available in its entirety only in the broadcast version, typically NTSC. In this work, we present a deep learning solution to bring the NTSC version to the new scan quality levels, which would be otherwise impossible with existing tools.

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References

  1. Michael Bernasconi, Abdelaziz Djelouah, Sally Hattori, and Christopher Schroers. 2020. Deep deinterlacing. In SMPTE Annual Technical Conf. Exhibition.Google ScholarGoogle Scholar
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  • Published in

    cover image ACM Conferences
    SIGGRAPH '22: ACM SIGGRAPH 2022 Talks
    July 2022
    108 pages
    ISBN:9781450393713
    DOI:10.1145/3532836

    Copyright © 2022 Owner/Author

    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 24 July 2022

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    • invited-talk
    • Research
    • Refereed limited

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    Overall Acceptance Rate1,822of8,601submissions,21%
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