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SMPTE Motion Imaging Journal ( Volume: 130, Issue: 3, April 2021)
[ACTIVE]

Automated Brand Color Accuracy for Realtime Video

Metadata

Publisher
SMPTE — White Plains, NY, USA
Doc Type
Journal Article
Content Type
Original Research
Abbreviated Title
SMPTE Mot. Imag. J
Volume
130, No. 3, pp. 45–49
Abstract
The perception of color in sports teams and branding alike is a highly valued asset in broadcasting and marketing that is worth protecting. The appearance of color is impacted by many factors including capture source, ambient light, and the limitations of screen technology. The current industry practice involves manual adjustment by a technician, who corrects the footage from a dozen or more camera feeds in realtime. This method adjusts the entire video frame based on the producer’s instructions. This paper presents a -pending, machine-learning approach titled “ColorNet” that targets brand color areas of a frame to output a color-corrected feed in realtime. Initial tests of ColorNet demonstrate the ability to reproduce manually created correction masks with high accuracy, while also being computationally efficient. Discussion includes system training and development and plans for a beta test where the system will be applied during a live broadcast of a sporting event in partnership with Clemson University Athletics.
Publication Date
2021-04-01
DOI
10.5594/JMI.2021.3058397
ISSN
Print: 1545-0279 | Electronic: 2160-2492
Link
https://doi.org/10.5594/JMI.2021.3058397
Author(s)
Emma Mayes
bio
Emma Mayes was the student team leader of the ColorNet research team for two years until she graduated from Clemson University, Clemson, SC, in December 2020 with a BS degree in industrial engineering. Falling in love with machine learning and artificial intelligence applications, she is currently working toward a PhD degree in industrial engineering at the University of Illinois at Urbana–Champaign, Champaign, IL, hoping to research the intersection between human factors and machine-learning applications. She is currently exploring natural language processing (NLP) applications in health care.
John Paul Lineberger
bio
John Paul Lineberger graduated with a BS degree in bioengineering in May 2020 from Clemson University, Clemson, SC. During his undergraduate studies, he spent years studying and designing machine-learning applications in precision agriculture, medical ultrasound imaging, and brand color management. He is continuing his studies as a PhD student in biomedical data science and informatics with Clemson University and the Medical University of South Carolina, Charleston, SC, where he is currently working on machine-learning models for lung cancer detection and cardiac modeling.
Michelle Mayer
bio
Michelle Mayer is a junior at Clemson University, Clemson, SC, studying computer engineering and minoring in mathematical sciences. At Clemson, she is a peer mentor for the Honors College, on the Outreach Team for her campus ministry, on the public relations team for the university’s student-led hackathon, a member of the Tau Beta Pi engineering honors society, and a member of the Society of Women Engineers. She joined the ColorNet research team during the spring of her freshman year when the project was just beginning, and over the past two years has gone from learning the basics of artificial intelligence to being a contributing developer for the project. She plans to graduate in May 2022 and go into industry. Her current technical interests are software engineering and embedded systems.
Andrew Sanborn
bio
Andrew Sanborn graduated in May 2020 from Clemson University, Clemson, SC, with a BS degree in computer science. He currently works for Epic Systems, Verona, WI, as a software developer.
Hudson Smith
bio
Hudson Smith received a bachelor’s degree in physics and mathematics from Erskine College, Due West, SC, and a PhD degree in physics from The Ohio State University, Columbus, OH, where he studied theoretical atomic physics with an emphasis on scientific computing. Following his graduate work, he entered the data science industry, applying statistics and machine-learning techniques to solve problems within the healthcare domain. He now leads the “Watson in the Watt” applied artificial intelligence (AI) program at the Watt Family Innovation Center with the mission of enabling novel and impactful applications of tools within the broader Clemson Community and equipping students to flourish as thinkers and doers in the realm of machine learning and artificial intelligence. As part of this program, he participates in more than a dozen projects spanning diverse fields such as politics, economics, agriculture, health care, and education.
Erica Walker
bio
Erica Walker has a diverse background in visual communications, which includes feature film production, web design and development, print buying, marketing, and graphic design for print. As a faculty member with the Department of Graphic Communications, College of Business, Clemson University, Clemson, SC, she teaches courses in photography, video, web development, and entrepreneurship. She is interested in many areas of research including software application, curriculum development, the entrepreneurial mindset, and artificial intelligence applications in color management for brands.
Keyword(s)
Artificial Intelligence, Color Management, Display, Machine Learning
Copyright
© 2021 SMPTE

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Emma Mayes, John Paul Lineberger, Michelle Mayer, Andrew Sanborn, Hudson Smith, and Erica Walker; Automated Brand Color Accuracy for Realtime Video, SMPTE Motion Imaging Journal ( Volume: 130, Issue: 3, April 2021); SMPTE, 2021. Available at https://doi.org/10.5594/JMI.2021.3058397
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Emma Mayes, John Paul Lineberger, Michelle Mayer, Andrew Sanborn, Hudson Smith, and Erica Walker; Automated Brand Color Accuracy for Realtime Video, SMPTE Motion Imaging Journal ( Volume: 130, Issue: 3, April 2021); SMPTE, 2021. Available at https://doi.org/10.5594/JMI.2021.3058397

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Emma Mayes, John Paul Lineberger, Michelle Mayer, Andrew Sanborn, Hudson Smith, and Erica Walker; Automated Brand Color Accuracy for Realtime Video, SMPTE Motion Imaging Journal ( Volume: 130, Issue: 3, April 2021); SMPTE, 2021. Available at https://doi.org/10.5594/JMI.2021.3058397
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<span class="citation">Emma Mayes, John Paul Lineberger, Michelle Mayer, Andrew Sanborn, Hudson Smith, and Erica Walker; <cite>Automated Brand Color Accuracy for Realtime Video</cite>, SMPTE Motion Imaging Journal ( Volume: 130, Issue: 3, April 2021); SMPTE, 2021. Available at <a href="https://doi.org/10.5594/JMI.2021.3058397" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2021.3058397</a></span>

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Emma Mayes, John Paul Lineberger, Michelle Mayer, Andrew Sanborn, Hudson Smith, and Erica Walker; Automated Brand Color Accuracy for Realtime Video, SMPTE Motion Imaging Journal ( Volume: 130, Issue: 3, April 2021); SMPTE, 2021
doi: 10.5594/JMI.2021.3058397
url: https://doi.org/10.5594/JMI.2021.3058397
Snippet:
<li>
Emma Mayes, John Paul Lineberger, Michelle Mayer, Andrew Sanborn, Hudson Smith, and Erica Walker; <cite id="bib-10-5594-jmi-2021-3058397">Automated Brand Color Accuracy for Realtime Video</cite>, SMPTE Motion Imaging Journal ( Volume: 130, Issue: 3, April 2021); SMPTE, 2021
<span class="doi">10.5594/JMI.2021.3058397</span>
</li>