codecs

Video: Scaling Video with AV1!

Posted on by Russell Trafford-Jones

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sense, particularly in the standard MPEG line where MPEG 2 gave way to AVC which gave way to HEVC which is now being superseded by VVC all of which achieved an approximately 50% compression improvement at the cost of a ten-fold computation increase.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

Posted on by Russell Trafford-Jones

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

Video: LCEVC, The Compression Enhancement Standard

Posted on by Russell Trafford-Jones

MPEG released 3 codecs last year, VVC, LCEVC and EVC. Which one was unlike the others? LCEVC is the only one that is an enhancement codec, working in tandem with a second codec running underneath. Each MPEG codec from last year addressed specific needs with VVC aiming at comprehensive bitrate savings while EVC aims to push encoding further whilst having a patent-free base layer.

In this talk, we hear from Guido Meardi from V-Nova who explains why LVECV is needed and how it works. LCEVC was made, Guido explains, to cater to an increasingly crowded network environment with more and more devices sending and receiving video both in residential and enterprise. LCEVC helps by reducing the bitrate needed for a certain quality level but, crucially, reduces the computation needed to achieve good quality video which not only benefits IoT and embedded devices but also general computing.

LCEVC uses a ‘base codec’ which is any other codec, often AVC or HEVC, which runs at a lower resolution than the source video. By using this hybrid technique, LCEVC aims to get the best video compression out of the codec yet by running the encode at a quarter resolution, allowing this to be done on low-power hardware. LCEVC then deals with reconstructing two enhancement layers and a, relatively simple, super-resolution upsample. This is all achieved with a simple toolset and all of the LCEVC computation can be done in CPU, GPU or other types of computation; it’s not bound to hardware acceleration.

Guido presents a number of results from tests against a whole range of codecs from VVC to AV1 to plain old AVC. These tests have been done by a number of people including Jan Ozer who undertook a whole range of tests. All of these tests point to the ability of LCEVC to extend bandwidth savings of existing codecs, new and old.

Guido shows an example of a video only comprising edges (apart from mid-grey) and says that LCEVC encodes this not only better than HEVC but also with an algorithm two orders of magnitude less. We then see an example of a pure upsample and an LCEVC encode. Upsampling alone can look good, but it can’t restore information and when there are small textual elements, the benefit of having an enhancement layer bringing those back into the upsampled video is clear.

On the decode side, Guido presents tests showing that decode is also quicker by at least two times if nor more, and because most of the decoding work is involved in decoding the base layer, this is still done using hardware acceleration (for AVC, HEVC and other codecs depending on platform). Because we can still rely on hardware decoding, battery life isn’t impacted.

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Speakers

Guido Meardi Guide Meardi
CEO & Co-Founder,
V-Nova

Video: Video Vectorisation

Posted on by Russell Trafford-Jones

Yesterday we learnt about machine learning improving VVC. But VVC has a fundamental property which limits its ability to compress: it’s raster-based. Vector graphics are infinitely scalable with no loss of quality and are very efficient. Instead of describing 100 individual pixels, you can just define a line 100 pixels long. This video introduces a vector-based video codec which dramatically reduces bitrate.

Sam Bhattacharyya from Vectorly introduces this technique which uses SVG graphics, a well-established graphics standard available in all major web browsers. It describes shapes with XML and is similar to WebGL. The once universal Adobe Flash was able to animate SVG shapes as part of its distinctive ‘flash animations’. The new aspect here is not to start with SVG shapes and animate them, but to create those shapes from video footage and recreate that same video but with vectors.

Sam isn’t shy to acknowledge that video vectorisation is a technique which works well on animation with solid colours; Peppa Pig being the example shown. But on more complex imagery without solid colours and sharp lines, this technique doesn’t result in useful compression. To deal with shaded animation, he explains a technique of using mesh gradients and diffusion curves to represent gradually changing colours and shades. Sam is interested in exploring a hybrid mode whereby traditional video had graphics overlayed using this low-bandwidth vector-based codec.

The technique uses machine learning/AI techniques to identify the shapes, track them and to put them in to keyframes. The codec plays this back by interpolating the motion. This can produce files playable at HD of only 100kbps. For the right content, this can be a great option given it’s based on established standards, is low bitrate and can be hardware accelerated.

Sam’s looking for interest from the community at large to help move this work forward.

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Speaker

Sam Bhattacharyya Sam Bhattacharyya
CEO, Co-founder
Vectorly