Video: S-Frame in AV1: Enabling better compression for low latency live streaming.

Streaming is such a success because it manages to deliver video even as your network capacity varies while you are watching. Called ABR (Adaptive Bitrate), this short talk asks how we can allow low-latency streams to nimbly adapt to network conditions whilst keeping the bitrate low in the new AV1 codec.

Tarek Amara from Twitch explains the idea in AV1 of introducing S-Frames, sometimes called ‘switch frames’, which take the role of the more traditional I or IDR frames. If a frame is marked as an IDR frame, this means the decoder knows it can start decoding from this frame without worrying that it’s referencing some data that came before this frame. By doing this, you can allow frequent points at which a decoder can enter a stream. IDR frames are typically I frames which are the highest bandwidth frames, by a large proportion. This is because they are a complete rendition of a frame without any of the predictions you find in P and B frames.

Because IDR frames are so large, if you want to keep overall bandwidth down, you should reduce the number of them. However, reducing the number of frames reduces the number if ‘in points’ for for the stream meaning a decoder then has to wait longer before it can start displaying the stream to the viewer. An S-Frame brings the benefits of an IDR in that it still marks a place in the stream where the decoder can join, free of dependencies on data previously sent. But the S-Frame is takes up much less space.

Tarek looks at how an S-Frame is created, the parameters it needs to obey and explains how the frames are signalled. To finish off he presents tests run showing the bitrate improvements that were demonstrated.
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Speaker

Tarek Amara Tarek Amara
Engineering Manager, Video Encoding,
Twitch

Video: Super Resolution – The scaler of tomorrow, here today!

If we ever had a time when most displays were the same resolution, those days are long gone with smartphone and tablets with extremely high pixel density nestled in with laptop screens of various resolutions and 1080-line TVs which are gradually being replaced with UHD variants. This means that HD videos are nearly always being upscaled which makes ‘getting upscaling right’ a really worthwhile topic. The well-known basic up/downscaling algorithms have been around for a while, and even the best-performing Lanczos is well over 20 years old. The ‘new kid on the block’ isn’t another algorithm, it’s a whole technique of inferring better upscaling using machine learning called ‘super resolution’.

Nick Chadwick from Mux has been running the code and the numbers to see how well super resolution works. Taking to the stage at Demuxed SF, he starts by looking at where scaling is used and what type it is. The most common algorithms are nearest neighbour, bi-cubic, bi-linear and lanczos with nearest neighbour being the most basic and least-well performing. Nick shows, using VMAF that using these for up and downscaling, that the traditional opinions of how well these algorithms perform are valid. He then introduces some test videos which are designed to let you see whether your video path is using bi-linear or bi-cubic upscaling, presenting his results of when bi-cubic can be seen (Safari on a MacBook Pro) as opposed to bi-linear (Chrome on a MacBook Pro). The test videos are available here.

In the next part of the talk, Nick digs a little deeper into how super resolution works and how he tested ffmpeg’s implementation of super resolution. Though he hit some difficulties in using this young filter, he is able to present some videos and shows that they are, indeed, “better to view” meaning that the text looks sharper and is easier to see with details being more easy pick out. It’s certainly possible to see some extra speckling introduced by the process, but VMAF score is around 10 points higher matching with the subjective experience.

The downsides are a very significant increase in computational power needed which limits its use in live applications plus there is a need for good, if not very good, understanding of ML concepts and coding. And, of course, it wouldn’t be the online streaming community if clients weren’t already being developed to do super-resolution on the decode despite most devices not being practically capable of it. So Nick finishes off his talk discussing what’s in progress and papers relating to the implementation of super resolution and what it can borrow from other developing technologies.

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Speaker

Nick Chadwick Nick Chadwick
Software Engineer,
Mux

Video: CMAF and DASH-IF Live ingest protocol

Of course without live ingest of content into the cloud, there is no live streaming so why would we leave such an important piece of the puzzle to an unsupported protocol like RTMP which has no official support for newer codecs. Whilst there are plenty of legacy workflows that still successfully use RTMP, there are clear benefits to be had from a modern ingest format.

Rufael Mekuria from Unified Streaming, introduces us to DASH-IF’s CMAF-based live ingest protocol which promises to solve many of these issues. Based on the ISO BMFF container format which underpins MPEG DASH. Whilst CMAF isn’t intrinsically low-latency, it’s able to got to much lower latencies than standard HLS, for instance.

This work to create a standard live ingest protocol was born out of an analysis, Rufael explains, of which part of the content delivery chain were most ripe for standardisation. It was felt that live ingest was an obvious choice partly because of the decaying RTMP protocol which was being sloppy replaced by individual companies doing their own thing, but also because there everyone contributing in the same way is of a general benefit to the industry. It’s not typically, at the protocol level, an area where individual vendors differentiate to the detriment of interoperability and we’ve already seen the, then, success of RMTP being used inter-operably between vendor equipment.

MPEG DAHS and HLS can be delivered in a pull method as well as pushed, but not the latter is not specified. There are other aspects of how people have ‘rolled their own’ which benefit from standardisation too such as timed metadata like ad triggers. Rufael, explaining that the proposed ingest protocol is a version of CMAF plus HTTP POST where no manifest is defined, shows us the way push and pull streaming would work. As this is a standardisation project, Rufael takes us through the timeline of development and publication of the standard which is now available.

As we live in the modern world, ingest security has been considered and it comes with TLS and authentication with more details covered in the talk. Ad insertion such as SCTE 35 is defined using binary mode and Rufael shows slides to demonstrate. Similarly in terms of ABR, we look at how switching sets work. Switching sets are sets of tracks that contain different representations of the same content that a player can seamlessly switch between.

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Speaker

Rufael Mekuria Rufael Mekuria
Head of Research & Standardisation,
Unified Streaming

Video: AV1 at Netflix

Netflix have continually been pushing forward video compression and analysis because their assets are played so many times that every bit saved is real money saved. VMAF is a great example of Netflix’s desire to push the state of the art forward. Developed by Netflix and two universities, this new objective metric allowed them to better evaluate the quality of videos using computer analysis and has continued to be the foundation of their work since.

One use of VMAF has been to verify the results of Netflix’s Per-Shot Encoding method which alters encoding parameters for each shot of the film rather than using a fixed set of parameters for the whole film. The Broadcast Knowledge has featured talks on their previous technique, per-title encoding (among others).

AV1, however must be the most famous innovation that Neflix is behind. A founding member of the Alliance for Open Media (AoM), Netflix saw a need a for a better codec and by making an open one, which also played to the needs of other internet giants such as Google, was a good way to create a vibrant community around it driving submissions to the codec itself but also, it is hoped, in the implementation and adoption.

In this two-part talk, LiWei Guo starts off by explaining the ways in which AV1 will be used by Netflix. Since this talk took place, Netflix has started streaming in AV1 to Android clients. LiWei points out that AV1 supports 10-bit video as standard – a notable difference from other codecs like AVC and HEVC. This allows Netflix to use 10-bit without worrying about decoder compatibility and he shows examples of skies and water which are significantly by the use of 10-bit.

Another feature of AV1 is the Film Grain synthesis which seeks to improve encoding efficiency by removing the random film grain of the source during the encode process then inserting a similar random noise on top to recreate the same look and feel. As anything random can’t be predicted, noise such of this is very wasteful for a codec to try and encode, therefore it’s not <a surprise that this can result in as much as a 30% reduction in bitrate. Before concluding, LiWei briefly explains per-shot encoding then shows data showing the overall improvements.

Andrey Norkin, also from Netflix explains their work with Intel on the SVT-AV1 software video encoder which leverages Intel’s SVT technology, a framework optimised for Xeon chips for video encoding and analysis. Netflix’s motivations are to further increase adoption by delivering a data centre-ready, optimised encoder and to create an AV1 encoder they can use to support their own internal research activities (did someone say AV2?). SVT allows for parallelisation, important for any computer nowadays with so many cores available.

Finishing up, Andrey points us to the Github repository, lets us know the development statement (as of November 2019) and looks at the speed increases that have taken off, comparing SVT-AV1 against the reference libaom encoder.

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Speakers

Andrey Norkin Andrey Norkin
Senior Research Scientist,
Netflix
LiWei Guo LiWei Guo
Senior Software Engineer,
Netflix