SMPTE Motion Imaging Journal ( Volume: 130, Issue: 8, September 2021)
[ACTIVE]
Utilizing Machine Learning for Video Processing
Metadata
- Publisher
- SMPTE — White Plains, NY, USA
- Doc Type
- Journal Article
- Content Type
- Original Research
- Abbreviated Title
- SMPTE Mot. Imag. J
- Volume
- 130, No. 8, pp. 107–111
- Abstract
- The demand for media content in the form of audio, video, and images is growing at an unprecedented rate. To meet this demand, media content production is increasing rapidly. However, the process of producing, distributing, and monetizing content is often complex, expensive, and timeconsuming. Applying machine learning (ML) capabilities can allow for media companies to augment the work their teams are doing and provide capabilities that were previously prohibitively expensive or not even possible. ML capabilities are made available in one of three ways—built into software as a feature, consumed as an artificial intelligence (AI, of which ML is a subset) service [application programming interfaces (APIs) that can be consumed without ML expertise], and custom-trained models (which require data science expertise to train and deploy models).
- Publication Date
- 2021-09-01
- DOI
10.5594/JMI.2021.3101580- ISSN
- Print:
1545-0279| Electronic:2160-2492 - Link
- https://doi.org/10.5594/JMI.2021.3101580
- Author(s)
- Liam Morrison
bio
Liam Morrison is a principal solutions architect focusing in data sciences and machine learning for media & entertainment (M&E). He has been with AWS for six years focusing on M&E, and has been helping customers build resilient, scalable, secure, and cost-effective applications utilizing machine learning. He advises on how emerging technologies can help create new products/experiences, add features to current products, streamline processes with automation, and leverage new engagement channels for consumers. - Copyright
- © 2021 SMPTE
Bibliographic Reference(s)
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- C. Ledig et al., “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” arXiv:1609.04802, May 2017. Accessed: Jun. 1, 2021. [Online]. Available: http://arxiv.org/abs/1609.04802 EXTERNAL
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- B. Joshi, K. Stewart, and D. Shapiro, “Bringing Impressionism to Life With Neural Style Transfer in Come Swim,” arXiv:1701.04928, Jan. 2017. Accessed: Jun. 1, 2021. [Online]. Available: http://arxiv.org/abs/1701.04928 EXTERNAL
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- H. Liu et al., “Video Super Resolution Based on Deep Learning: A Comprehensive Survey,” arXiv:2007.12928, Dec. 2020. Accessed: Jun. 1, 2021. [Online]. Available: http://arxiv.org/abs/2007.12928 EXTERNAL
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- Topaz Labs, “Video Enhance AI.” Accessed: Jun. 1, 2021. [Online]. Available: https://www.topazlabs.com/video-enhance-ai EXTERNAL
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Liam Morrison; Utilizing Machine Learning for Video Processing, SMPTE Motion Imaging Journal ( Volume: 130, Issue: 8, September 2021); SMPTE, 2021. Available at https://doi.org/10.5594/JMI.2021.3101580
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<span class="citation">Liam Morrison; <cite>Utilizing Machine Learning for Video Processing</cite>, SMPTE Motion Imaging Journal ( Volume: 130, Issue: 8, September 2021); SMPTE, 2021. Available at <a href="https://doi.org/10.5594/JMI.2021.3101580" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2021.3101580</a></span>
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Liam Morrison; Utilizing Machine Learning for Video Processing, SMPTE Motion Imaging Journal ( Volume: 130, Issue: 8, September 2021); SMPTE, 2021
doi: 10.5594/JMI.2021.3101580
url: https://doi.org/10.5594/JMI.2021.3101580
doi: 10.5594/JMI.2021.3101580
url: https://doi.org/10.5594/JMI.2021.3101580
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<li> Liam Morrison; <cite id="bib-10-5594-jmi-2021-3101580">Utilizing Machine Learning for Video Processing</cite>, SMPTE Motion Imaging Journal ( Volume: 130, Issue: 8, September 2021); SMPTE, 2021 <span class="doi">10.5594/JMI.2021.3101580</span> </li>