Non-iterative Content-Adaptive Distributed Encoding Through ML Techniques

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Publisher: SMPTE — White Plains, NY, USA
Doc Type: Conference Paper
Content Type: Original Research
Volume: 2017, No. 10, pp. 1–8
Abstract: Distributed encoding is desired in content preparation workflows in the cloud to reduce turnaround times. Content-adaptive bit allocation at title, chunk, and even frame level, as opposed to using a fixed ladder of content-independent bit-rates and resolutions, 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 over 2-pass Variable bit-rate (VBR) encoders. With live use-cases attempting to use a set of distributed encoders to achieve higher bit-rate savings (both through use of higher compression presets and through content-adaptive bit allocation) and consistent quality, there is a need to limit the increase in computational complexity. In this paper, we propose a non-iterative codec-agnostic approach that employs machine learning techniques to perform consistent quality content-adaptive encoding within the constraints of a maximum bit-rate in a manner that makes it equally suitable for live and on-demand workflows. The method has the ability to take a target subjective rating level and allocate appropriate bits for each group of frames to achieve that. The proposed approach also anticipates automatic selection of the right resolution and frame-rate for a given representation within an ABR set, and content-specific encoding parameters to maximize the bit savings and/or visual quality. Test results are presented over a wide range of content types comparing the performance of the proposed approach against 2-pass VBR methods. Initial results indicate that the proposed approach can recover ~85% of the bit-savings possible with exhaustive techniques. The computational complexity of the proposed approach is only 15-20% of 2-pass VBR encoding.
Publication Date: 2017-10-23
DOI: 10.5594/M001783
ISBN: Print: 978-1-61482-959-1
Link: https://doi.org/10.5594/M001783
Author(s): Sriram SethuramanIttiam Systems Pvt. Ltd., Bangalore, India
Nithya V. S.Ittiam Systems Pvt. Ltd., Bangalore, India
Venkata Narayanababu Laveti DIttiam Systems Pvt. Ltd., Bangalore, India
Keyword(s): Distributed Encoding, Content-adaptive bit-rate/resolution Ladder For Adaptive Bit-rate (ABR) Streaming, HLS, DASH, Machine Learning, Consistent Quality Encoding, Noniterative, Live and On-demand Over-the-top (OTT) Streaming, Content storage/delivery Efficiency
Copyright: © 2017 Society of Motion Picture and Television Engineers, Inc.

Bibliographic Reference(s)

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Sriram Sethuraman, Nithya V. S., and Venkata Narayanababu Laveti D; Non-iterative Content-Adaptive Distributed Encoding Through ML Techniques, SMPTE Meetings and Conferences ( October 2017); SMPTE, 2017, ISBN: 978-1-61482-959-1. Available at https://doi.org/10.5594/M001783

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Sriram Sethuraman, Nithya V. S., and Venkata Narayanababu Laveti D; Non-iterative Content-Adaptive Distributed Encoding Through ML Techniques, SMPTE Meetings and Conferences ( October 2017); SMPTE, 2017, ISBN: 978-1-61482-959-1. Available at https://doi.org/10.5594/M001783

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<span class="citation">Sriram Sethuraman, Nithya V. S., and Venkata Narayanababu Laveti D; <cite>Non-iterative Content-Adaptive Distributed Encoding Through ML Techniques</cite>, SMPTE Meetings and Conferences ( October 2017); SMPTE, 2017, ISBN: 978-1-61482-959-1. Available at <a href="https://doi.org/10.5594/M001783" target="_blank" rel="noopener">https://doi.org/10.5594/M001783</a></span>

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Sriram Sethuraman, Nithya V. S., and Venkata Narayanababu Laveti D; <cite id="bib-10-5594-m001783">Non-iterative Content-Adaptive Distributed Encoding Through ML Techniques</cite>, SMPTE Meetings and Conferences ( October 2017); SMPTE, 2017, ISBN: 978-1-61482-959-1
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