Noniterative Content-Adaptive Distributed Encoding Through ML Techniques

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Publisher: SMPTE — White Plains, NY, USA
Doc Type: Journal Article
Content Type: Original Research
Abbreviated Title: SMPTE Mot. Imag. J
Volume: 127, No. 9, pp. 50–55
Abstract: Distributed encoding is desirable for content preparation cloud workflows to reduce turnaround times. Content-adaptive bit allocation strategies have been proposed to achieve efficiencies in storage and delivery. Many of these methods tend to be iterative in nature and consume significant additional compute resources. There is a need to limit this increase in computational complexity. In this paper, we propose a noniterative codec-agnostic approach that employs machine learning techniques to achieve average bitrate savings and a target consistent quality by selecting a content-adaptive bitrate and resolution for each adaptive bitrate (ABR) segment within each ABR representation in a manner that makes it equally suitable for live and on-demand workflows. Test results are presented over a wide range of content types. Initial results indicate that the proposed approach can recover ~85% of the bit savings possible with more exhaustive techniques while its computational complexity is only 15%–20% of two-pass variable bitrate (VBR) encoding.
Publication Date: 2018-10-01
DOI: 10.5594/JMI.2018.2862647
ISSN: Print: 1545-0279 | Electronic: 2160-2492
Link: https://doi.org/10.5594/JMI.2018.2862647
Author(s): Sriram Sethuraman
V. S. Nithya
D. Venkata Narayanababu Laveti
Keyword(s): Consistent Quality Encoding, Content-Adaptive Encoding, Content storage/delivery Efficiency, Distributed Encoding, DASH, HLS, Live and On-demand Over-the-top (OTT) Streaming, Machine Learning, Noniterative
Copyright: © 2018 SMPTE

Bibliographic Reference(s)

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Sriram Sethuraman, V. S. Nithya, and D. Venkata Narayanababu Laveti; Noniterative Content-Adaptive Distributed Encoding Through ML Techniques, SMPTE Motion Imaging Journal ( Volume: 127, Issue: 9, October 2018); SMPTE, 2018. Available at https://doi.org/10.5594/JMI.2018.2862647

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Sriram Sethuraman, V. S. Nithya, and D. Venkata Narayanababu Laveti; Noniterative Content-Adaptive Distributed Encoding Through ML Techniques, SMPTE Motion Imaging Journal ( Volume: 127, Issue: 9, October 2018); SMPTE, 2018. Available at https://doi.org/10.5594/JMI.2018.2862647

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<span class="citation">Sriram Sethuraman, V. S. Nithya, and D. Venkata Narayanababu Laveti; <cite>Noniterative Content-Adaptive Distributed Encoding Through ML Techniques</cite>, SMPTE Motion Imaging Journal ( Volume: 127, Issue: 9, October 2018); SMPTE, 2018. Available at <a href="https://doi.org/10.5594/JMI.2018.2862647" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2018.2862647</a></span>

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Sriram Sethuraman, V. S. Nithya, and D. Venkata Narayanababu Laveti; Noniterative Content-Adaptive Distributed Encoding Through ML Techniques, SMPTE Motion Imaging Journal ( Volume: 127, Issue: 9, October 2018); SMPTE, 2018
doi: 10.5594/JMI.2018.2862647
url: https://doi.org/10.5594/JMI.2018.2862647

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Sriram Sethuraman, V. S. Nithya, and D. Venkata Narayanababu Laveti; <cite id="bib-10-5594-jmi-2018-2862647">Noniterative Content-Adaptive Distributed Encoding Through ML Techniques</cite>, SMPTE Motion Imaging Journal ( Volume: 127, Issue: 9, October 2018); SMPTE, 2018
<span class="doi">10.5594/JMI.2018.2862647</span>
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