Video: AV1 – A Reality Check

Released in 2018, AV1 had been a little over two years in the making at the Alliance of Open Media founded by industry giants including Google, Amazon, Mozilla, Netflix. Since then work has continued to optimise the toolset to bring both encoding and decoding down to real-world levels.

This talk brings together AOM members Mozilla, Netflix, Vimeo and Bitmovin to discus where AV1’s up to and to answer questions from the audience. After some introductions, the conversation turns to 8K. The Olympics are the broadcast industry’s main driver for 8K at the moment, though it’s clear that Japan and other territories aim to follow through with further deployments and uses.

“AV1 is the 8K codec of choice” 

Paul MacDougall, Bitmovin
 CES 2020 saw a number of announcements like this from Samsung regarding AV1-enabled 8K TVs. In this talk from Google, Matt Frost from Google Chrome Media explains how YouTube has found that viewer retention is higher with VP9-delivered videos which he attributes to VP9’s improved compression over AVC which leads to quicker start times, less buffering and, often, a higher resolution being delivered to the user. AV1 is seen as providing these same benefits over AVC without the patent problems that come with HEVC.

 
It’s not all about resolution, however, points out Paul MacDougall from BitMovin. Resolution can be useful, for instance in animations. For animated content, resolution is worth having because it accentuates the lines which add intelligibility to the picture. For some content, with many similar textures, grass, for instance, then quality through bitrate may be more useful than adding resolution. Vittorio Giovara from Vimeo agrees, pointing out that viewer experience is a combination of many factors. Though it’s trivial to say that a high-resolution screen of unintended black makes for a bad experience, it is a great reminder of things that matter. Less obviously, Vittorio highlights the three pillars of spatial, temporal and spectral quality. Temporal refers to upping the bitrate, spatial is, indeed, the resolution and spectral refers to bit-depth and colour-depth know as HDR and Wide Colour Gamut (WCG).

Nathan Egge from Mozilla acknowledges that in their 2018 code release at NAB, the unoptimized encoder which was claimed by some to be 3000 times slower than HEVC, was ’embarrassing’, but this is the price of developing in the open. The panel discusses the fact that the idea of developing compression is to try out approaches until you find a combination that work well. While you are doing that, it would be a false economy to be constantly optimising. Moreover, Netflix’s Anush Moorthy points out, it’s a different set of skills and, therefore, a different set of people who optimise the algorithms.

Questions fielded by the panel cover whether there are any attempts to put AV1 encoding or decoding into GPU. Power consumption and whether TVs will have hardware or software AV1 decoding. Current in-production AV1 uses and AVC vs VVC (compression benefit Vs. royalty payments).

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Speakers

Vittorio Giovara Vittorio Giovara
Manager, Engineering – Video Technology
Vimeo
Nathan Egge Nathan Egge
Video Codec Engineer,
Mozilla
Paul MacDougall Paul MacDougall
Principal Sales Engineer,
Bitmovin
Anush Moorthy Anush Moorthy
Manager, Video and Image Encoding
Netflix
Tim Siglin Tim Siglin
Founding Executive Director
Help Me Stream, USA

Video: QoE Impact from Router Buffer sizing and Active Queue Management

Netflix take to the stage at Demux to tell us about the work they’ve been doing to understand and reduce latency by looking at the queue management of their managed switches. As Tony Orme mentioned yesterday, we need buffers in IP systems to allow synchronous parts to interact. Here, we’re looking at how the core network fabric’s buffers can get in
the way of the main video flows.

Te-Yuan Huang from Netflix explains their work in investigating buffers and how best to use them. She talks about the flows that occur due to the buffer models of standard switches i.e. waiting until the buffer is full and then dropping everything else that comes in until the buffer is emptied. There is an alternative method, Active Queue Management (AQM), called FQ-CoDel which drops packets based on probability before the buffer is dropped. By carefully choosing the probability, you can actually improve buffer handling and the impact it has on latency.

Te-Yuan shows us results from tests that her team has done which show that the FQ-CoDel specification does, indeed, reduce latency. After showing us the data, she summarises saying that FQ-CoDel improves playback and QOE.

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Speaker

Te-Yuan Huang Te-Yuan Huang
Engineering Manager (Adaptive Streaming),
Netflix

Video: Buffer Sizing and Video QoE Measurements at Netflix

At a time when Netflix is cutting streaming quality to reduce bandwidth, we take a look at the work that’s gone into optimising latency within the switch at ISPs which was surprisingly high.

Bruce Spang interned at Netflix and studied the phenomenon of unexpected latency variation within the netflix caches they deploy at ISPs to reduce latency and bandwidth usage. He starts by introducing us to the TCP buffering models looking at how they work and what they are trying to achieve with the aim of identifying how big it is supposed to be. The reason this is important is that if it’s a big buffer, you may find that data takes a long time to leave the buffer when it gets full, thus adding latency to the packets as they travel through. Too small, of course, and packets have to be dropped. This creates more rebuffing which impacts the ABR choice leading to lower quality.

Bruce was part of an experiment that studied whether the buffer model in use behaved as expected and whist he found that it did most of the time, he did find that video performance varied which was undesirable. To explain this, he details the testing they did and the finding that congestion, as you would expect, increases latency more during a congested time. Moreover, he showed that a 500MB had more latency than 50MB.

To explain the unexplained behaviour such as long-tail content having lower latency than popular content, Bruce explains how he looked under the hood of the router to see how VOQs are used to create queues of traffic and how they work. Seeing the relatively simply logic behind the system, Bruce talks about the results they’ve achieved working with the vendor to improve the buffering logic.

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Speakers

Bruce Spang Bruce Spang
PhD Student, Stanford

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