In the ongoing battle to find the minimum bitrate for good looking video, automation is key to achieving this quickly and cheaply. However, metrics like PSNR don’t always give the best answers meaning that eyes are still better the job than silicon.
In this talk from the Demuxed conference, Intel’s Vasavee Vijayaraghavan shows us examples of computer analysis failing to identify lowest bitrate leaving the encoder spending many megabits encoding video so that it looks imperceptibly better. Further more it’s clear that MOS – the Mean Opinion Score – which has a well defined protocol behind it continues to produce the best results, though setting up and co-ordinating takes orders of magnitude more time and money.
Vasavee shows how she’s managed to develop a hybrid workflow which combines metrics and MOS scores to get much of the benefit of computer-generated metrics fed into the manual MOS process. This allows a much more targeted subjective perceptual quality MOS process thereby speeding up the whole process but still getting that human touch where it’s most valuable.
Netflix’s Anne Aaron explains how VMAF came about and how AV1 is going to benefit both the business and the viewers.
VMAF is a method for computers to calculate the quality of a video in a way which would match a human’s opinion. Standing for Video Multi-Method Assessment Fusion, Anne explains that it’s a combination (fusion) of more than one metric each harnessing different aspects. She presents data showing the increased correlation between VMAF and real-life tests.
Anne’s job is to maximise enjoyment of content through efficient use of bandwidth. She explains there are many places with wireless data is limited so getting the maximum amount of video through that bandwidth cap is an essential part of Netflix’s business health.
This ties in with why Netflix is part of the Alliance for Open Media who are in the process of specifying AV1, the new video codec which promises bitrate improvements over-and-above HEVC. Anne expands on this and presents the aim to deliver 32 hours of video using AV1 for 4Gb subscribers.
MUX is a very pro-active company pushing forward streaming technology. At NAB 2019 they have announced Audience Adaptive Encoding which is offers encodes tailored to both your content but also the typical bitrate of your viewing demographic. Underpinning this technology is machine learning and their Per-title encoding technology which was released last year.
This talk with Nick Chadwick looks at what per-title encoding is, how you can work out which resolutions and bitrates to encode at and how to deliver this as a useful product.
Nick takes some time to explain MUX’s ‘convex hulls’ which give a shape to the content’s performance at different bitrates and helps visualise the optimum encoding parameters the content. Moreover we see that using this technique, we see some surprising circumstances when it makes sense to start at high resolutions, even for low bitrates.
Looking then at how to actually work out on a title-by-title basis, Nick explains the pros and cons of the different approaches going on to explain how MUX used machine learning to generate the model they created to make this work.
Finishing off with an extensive Q&A, this talk is a great overview on how to pick great encoding parameters, manually or otherwise.
Streaming Media East brings together Beamr, Netflix BAMTECH Media and SSIMWAVE to discuss the best ways to evaluate software encoders and we see there is much overlap with hardware encoder evaluation, too.
The panel gets into detail covering:
Choosing source sequences
Rate Control Modes
Bit Rate or Quality Target Levls
Offline (VOD) vs Live (Linear)
Discrete vs. Multi-resolution/Bitrate
Subjective vs. objective measurements
Encoding Efficiency vs Performance
Video vs Still frames
Evaluation at Encode Resolution Vs Display Resolution
Dr. Anne Aaron
Director of Video Algorithms,
VP Video Processing, Core Media Video Processing,
Dr. Zhou Wang
Chief Science Officer,
Moderator: Tom Vaughan
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