Optimizing Transcoder Quality Targets Using a Neural Network with an Embedded Bitrate Model
Michele Covell, Martín Arjovsky, Yao-Chung Lin, Anil C. Kokaram · Electronic Imaging · 2016
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%.