Back to articles
Articles
Volume: 28 | Article ID: art00016
Image
Optimizing Transcoder Quality Targets Using a Neural Network with an Embedded Bitrate Model
  DOI :  10.2352/ISSN.2470-1173.2016.2.VIPC-237  Published OnlineFebruary 2016
Abstract

Like all modern internet-based video services, YouTube employs adaptive bitrate (ABR) streaming. Due to the computational expense of transcoding, the goal is to achieve a target bitrate for each ABR segment, without requiring multi-pass encoding. We extend the content-dependent model equation between bitrate and frame rate [6] to include CRF and frame size. We then attempt to estimate the content-dependent parameters used in the model equation, using simple summary features taken from the video segment and a novel neural-network layout. We show that we can estimate the correct quality-control parameter on 65% of our test cases without using a previous transcode of the video segment. If there is a previous transcode of the same segment available (using an inexpensive configuration), we increase our accuracy to 80%.

Subject Areas :
Views 38
Downloads 9
 articleview.views 38
 articleview.downloads 9
  Cite this article 

Michele Covell, Martín Arjovsky, Yao-chung Lin, Anil Kokaram, "Optimizing Transcoder Quality Targets Using a Neural Network with an Embedded Bitrate Modelin Proc. IS&T Int’l. Symp. on Electronic Imaging: Visual Information Processing and Communication VII,  2016,  https://doi.org/10.2352/ISSN.2470-1173.2016.2.VIPC-237

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2016
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA