Video: AV1 and ARM

AV1’s no longer the slow codec it was when it was released. Real-time encodes and decodes are now practical with open-source software implementations called rav1e for encoding and dav1d for decoding. We’ve also seen in previous talks the SVT-AV1 provides real-time encoding and WebRTC now has a real-time version with the AV1 codec.

In this talk, rav1e contributor Vibhoothi explains more about these projects and how the ARM chipset helps speed up encoding. The Dav1d started project started in 2018 with the intention of being a fast, cross-platform AV1 encoder with a small binary which Vibhoothi says is exactly what we have in 2021. Dav1d is the complementary decoder project. AV1 decoding is found in many places now including in Android Q, in Microsoft’s media extension for it, VLC supports AV1 on linux and macOS thanks to dav1d, AV1 is supported in all major browsers, on NVIDIA and AMD GPUs plus Intel Tiger Lake CPUs. Netflix even use dav1d to stream AV1 onto some mobile devices. Overall, then, we see that AV1 has ‘arrived’ in the sense that it’s in common and increasing use.

The ARM CPU architecture underpins nearly all smartphones and most tablets so ARM is found in a vast number of devices. It’s only relatively recently that ARM has made it into mainstream servers. One big milestone has been the release of Neoverse which is an ARM chip for infrastructure. AWS now offer ARM instances that have a 40% higher performance but a 20% reduced cost. These have been snapped up by Netflix but also by a plethora of non-media companies. Recently Apple has made waves with their introduction of the M1 ARM-based chip for desktops which has benchmarks far in excess of the previous x86 offering which shows that the future for ARM-based implementations of the rav1e encoder and dav1d decoder are bright.

Vibhoothi outlines how dav1d works better on ARM then x86 with improved threading support including hand-written asm optimisations and support for 10-bit assembly. rav1e has wide support in VLC, GStreamer, FFmpeg, libavif and others.

The talk finishes with a range of benchmarks showing how better-than-real-time encoding and decoding is possible and how the number of threads relates to the throughput. Vibhoothi’s final thoughts focus on what’s still missing in the ARM implementations.

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Speaker

Vibhoothi Vibhoothi
Developer, VideoLAN
Research Assistant, Trinity College Dublin,
Codec Development, rav1e, Mozilla

Video: Comparison of EVC and VVC against HEVC and AV1

AV1’s royalty-free status continues to be very appealing, but in raw compression is it losing ground now to the newer codecs such as VVC? EVC has also introduced a royalty-free model which could also detract from AV1’s appeal and certainly is an improvement over HEVC’s patent debacle. We have very much moved into an ecosystem of patents rather than the MPEG2/AVC ‘monoculture’ of the 90s within broadcast. What better way to get a feel for the codecs but to put them to the test?

Dan Grois from Comcast has been looking at the new codecs VVC and EVC against AV1 and HEVC. VVC and EVC were both released last year and join LCEVC as the three most recent video codecs from MPEG (VVC was a collaboration between MPEG and IRU). In the same way, HEVC is known as H.265, VVC can be called H.266 and it draws its heritage from the HEVC too. EVC, on the other hand, is a new beast whose roots are absolutely shared with much of MPEG’s previous DCT-based codecs, but uniquely it has a mode that is totally royalty-free. Moreover, its high-performant mode which does include patented technology can be configured to exclude any individual patents that you don’t wish to use thus adding some confidence that businesses remain in control of their liabilities.

Dan starts by outlining the main features of the four codecs discussing their partitioning methods and prediction capabilities which range from inter-picture, intra-picture and predicting chroma from the luma picture. Some of these techniques have been tackled in previous talks such as this one, also from Mile High Video and this EVC overview and, finally, this excellent deep dive from SMPTE in to all of the codecs discussed today plus LCEVC.

Dan explains the testing he did which was based on the reference encoder models. These are encoders that implement all of the features of a codec but are not necessarily optimised for speed like a real-world implementation would be. Part of the work delivering real-world implementations is using sophisticated optimisations to get the maths done quickly and some is choosing which parts of the standard to implement. A reference encoder doesn’t skimp on implementation complexity, and there is seldom much time to optimise speed. However, they are well known and can be used to benchmark codecs against each other. AV1 was tested in two configurations since

AV1 needs special treatment in this comparison. Dan explains that AV1 doesn’t have the same approach to GOPs as MEPG so it’s well known that fixing it’s QP will make it inefficient, however, this is what’s necessary for a fair comparison so, in addition to this, it’s also run in VBR mode which allows it to use its GOP structure to the full such as AV1’s invisible frames which carry data which can be referenced by other frames but which are never actually displayed.

The videos tested range from 4K 10bit down to low resolution 8 bit. As expected VVC outperforms all other codecs. Against HEVC, it’s around 40% better though carrying with it a factor of 10 increase in encoding complexity. Note that these objective metrics tend to underrepresent subjective metrics by 5-10%. EVC consistently achieved 25 to 30% improvements over HEVC with only 4.5x the encoder complexity. As expected AV1’s fixed QP mode underperformed and increased data rate on anything which wasn’t UHD material but when run in VBR mode managed 20% over HEVC with only a 3x increase in complexity.

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Speaker

Dan Grois Dan Grois
Principal Researcher,
Comcast

Video: Scaling Video with AV1!

A nuanced look at AV1. If we’ve learnt one thing about codecs over the last year or more, it’s that in the modern world pure bitrate efficiency isn’t the only game in town. JPEG 2000 and, now, JPEG XS, have always been excused their high bitrate compared to MPEG codecs because they deliver low latency and high fidelity. Now, it’s clear that we also need to consider the computational demand of codec when evaluating which to use in any one situation.

John Porterfield welcomes Facebook’s David Ronca to understand how AV1’s arriving on the market. David’s the director of Facebook’s video processing team, so is in pole position to understand how useful AV1 is in delivering video to viewers and how well it achieves its goals. The conversation looks at how to encode, the unexpected ways in which AV1 performs better than other codecs and the state of the hardware and software decoder ecosystem.

David starts by looking at the convex hull, explaining that it’s a way of encoding content multiple times at different resolutions and bitrates and graphing the results. This graph allows you to find the best combination of bitrate and resolution for a target quality. This works well, but the multiple encodes burdens the decision with a lot of extra computation to get the best set of encoding parameters. As proof of its effectiveness, David cites a time when a 200kbps max target was given for and encoder of video plus audio. The convex hull method gave a good experience for small screens despite the compromises made in encoding fidelity. The important part is being flexible on which resolution you choose to encode because by allowing the resolution to drift up or down as well as the bitrate, higher fidelity combinations can be found over keeping the resolution fixed. This is called per-title encoding and was pioneered by Netflix as discussed in the linked talk, where David previously worked and authored this blog post on the topic.

It’s an accepted fact that encoder complexity increases for every generation. Whilst this makes sense, particularly in the standard MPEG line where MPEG 2 gave way to AVC which gave way to HEVC which is now being superseded by VVC all of which achieved an approximately 50% compression improvement at the cost of a ten-fold computation increase. But David contends that this buries the lede. Whilst it’s true that the best (read: slowest) compression improves by 50% and has a 10% complexity increase, it’s often missed that at the other end of the curve, one of the fastest settings of the newer codec can now match the best of the old codec with a 90% reduction in computation. For companies working in the software world encoding, this is big news. David demonstrates this by graphing the SVT-AV1 encoder against the x265 HEVC encoder and that against x264.

David touches on an important point, that there is so much video encoding going on in the tech giants and distributed around the world, that it’s important for us to keep reducing the complexity year on year. As it is now, with the complexity increasing with each generation of encoder, something has to give in the future otherwise complexity will go off the scale. The Alliance for Open Media’s AV1 has something to say on the topic as it’s improved on HEVC with only a 5% increase in complexity. Other codecs such as MPEG’s LCEVC also deliver improved bitrate but at lower complexity. There is a clear environmental impact from video encoding and David is focused on reducing this.

AOM is also fighting the commercial problem that codecs have. Companies don’t mind paying for codecs, but they do mind uncertainty. After all, what’s the point in paying for a codec if you still might be approached for more money. Whilst MPEG’s implementation of VVC and EVC aims to give more control to companies to help them control their risk, AOM’s royalty-free codec with a defence fund against legal attacks, arguably, gives the most predictable risk of all. AOM’s aim, David explains, is to allow the web to expand without having to worry about royalty fees.

Next is some disappointing news for AV1 fans. Hardware decoder deployments have been delayed until 2023/24 which probably means no meaningful mobile penetration until 2026/27. In the meantime the very good dav1d decoder and also gav1 are expected to fill the gap. Already quite fast, the aim is for them to be able to do 720p60 decoding for average android devices by 2024.

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Speakers

David Ronca David Ronca
Director, Video Encoding,
Facebook
John Porterfield
Freelance Video Webcast Producer and Tech Evangelist
JP’sChalkTalks YouTube Channel

Video: Decoder Complexity Aware AV1 Encoding Optimization

AV1’s been famous for very low encoding speed, but as we’ve seen from panel like this, AV1 encoding times have dropped into a practical range and it’s starting to gain traction. Zoe Liu, CEO of Visionular, is here to talk at Mile High Video 2020 about how careful use of encoding parameters can deliver faster encodes, smooth decodes, and yet balance that balance with codec efficiency.

Zoe starts by outlining the good work that’s been done with the SVT-AV1 encoder which leaves it ready for deployment, as we heard previously from David Ronca of Facebook. Similarly the Dav1d decoder has recently made many speed improvements, now being able to easily decode 24fps on mobiles using between 1.5 and 3 Snapdragon cores depending on resolution. Power consumption has been measured as higher than AVC decoding but less than HEVC. Further to that, hardware support is arriving in many devices like TVs.

Zoe then continues to show ways in which encoding can be sped up by reducing the calculations done which, in turn, increased decoder speed. Zoe’s work has exposed settings that significantly speed up decoding but have very little effect on the compression efficiency of the codec which opens up use cases where decoding was the blocker and a 5% reduction in the ability to compress is a price worth paying. One example cited is ignoring partition sizes of less than 8×8. These small partitions can be numerous and bog down calculations but their overall contribution to bitrate reduction is very low.

All of these techniques are brought together under the heading of Decoder Complexity Aware AV1 Encoding Optimization which, Zoe explains, can result in an encoding speed-up of over two times the original framerate i.e. twice real-time on an Intel i5. Zoe concludes that this creates a great opportunity to apply AV1 to VOD use cases.

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Speaker

Zoe Liu Zoe Liu
CEO,
Visionular