Dan Grois

Video: Comparison of EVC and VVC against HEVC and AV1

Posted on by Russell Trafford-Jones

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 ITU). 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 MPEG so it’s well known that fixing its 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: Deep Neural Networks for Video Coding

Posted on by Russell Trafford-Jones

We know AI is going to stick around. Whether it’s AI, Machine Learning, Deep Learning or by another name, it all stacks up to the same thing: we’re breaking away from fixed algorithms where one equation ‘does it all’ to a much more nuanced approached with a better result. This is true across all industries. Within the Broadcast industry, one way it can be used is in video and audio compression. Want to make an image smaller? Downsample it with a Convolutional Neural Network and it will look better than Lanczos. No surprise, then, that this is coming in full force to a compression technology near you.

In this talk from Comcast’s Dan Grois, we hear the ongoing work to super-charge the recently released VVC by replacing functional blocks with neural-networks-based technologies. VVC has already achieved 40-50% improvements over HEVC. From the work Dan’s involved with, we hear that more gains are looking promising by using neural networks.

Dan explains that deep neural networks recognise images in layers. The brain does the same thing having one area sensitive to lines and edges, another to objects, another part of the brain to faces etc. A Deep Neural Network works in a similar way.
 

 

During the development of VVC, Dan explains, neural network techniques were considered but deemed too memory- or computationally-intensive. Now, 6 years on from the inception of VVC, these techniques are now practical and are likely to result in a VVC version 2 with further compression improvements.

Dan enumerates the tests so far swapping out each of the functional blocks in turn: intra- and inter-frame prediction, up- and down-scaling, in-loop filtering etc. He even shows what it would look like in the encoder. Some blocks show improvements of less than 5%, but added together, there are significant gains to be had and whilst this update to VVC is still in the early stages, it seems clear that it will provide real benefits for those that can implement these improvements which, Dan highlights at the end, are likely to require more memory and computation than the current version VVC. For some, this will be well worth the savings.

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Speaker

Dan Grois Dan Grois
Principal Researcher,
Comcast

Video: Deep Neural Networks for Video Coding

Posted on by Russell Trafford-Jones

Artificial Intelligence, Machine Learning and related technologies aren’t going to go away…the real question is where they are best put to use. Here, Dan Grois from Comcast shows their transformative effect on video.

Some of us can have a passable attempt at explaining what neural networks, but to start to understand how this technology works understanding how our neural networks work is important and this is where Dan starts his talk. By walking us through the workings of our own bodies, he explains how we can get computers to mimic parts of this process. This all starts by creating a single neuron but Dan explains multi-layer perception by networking many together.

As we see examples of what these networks are able to do, piece by piece, we start to see how these can be applied to video. These techniques can be applied to many parts of the HEVC encoding process. For instance, extrapolating multiple reference frames, generating interpolation filters, predicting variations etc. Doing this we can achieve a 10% encoding improvements. Indeed, a Deep Neural Network (DNN) can totally replace the DCT (Discrete Cosine Transform) widely used in MPEG and beyond. Upsampling and downsampling can also be significantly improved – something that has already been successfully demonstrated in the market.

Dan isn’t shy of tackling the reason we haven’t seen the above gains widely in use; those of memory requirements and high computational costs. But this work is foundational in ensuring that these issues are overcome at the earliest opportunity and in optimising the approach to implementing them to the best extent possible to day.

The last part of the talk is an interesting look at the logical conclusion of this technology.

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Speaker

Dan Grois Dan Grois
Principal Researcher
Comcast

Video: HEVC/H.265 Video Coding Standard

Posted on by Russell Trafford-Jones

HEVC, also known as H.265 is often discussed even many years after its initial release fro MPEG with some saying that people aren’t using it and others saying its gaining traction. In reality, both sides have a point. Increasingly HEVC is being adopted partly because of wider implementation in products and partly because of a continued push toward higher resolution video which often gives the opportunity to make a clean break from AVC/H.264/MPEG 4.

This expert-led talk looks in detail at HEVC and how it’s constructed. For some, the initial part of the video will be enough. Others will want to bookmark the video to use as reference in their work, whilst still others will want to watch the whole things and will immediately find it puts parts of their work in better context.

Wherever you fit, I think you’ll agree this is a great resource for understanding HEVC streams enabling you to better troubleshoot problems.

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Speakers

David Marpe David Marpe
Head of Department Video Coding & Analytics,
Fraunhofer Heinrich Hertz Institute
Karsten Suehring Karsten Suehring
Project Manager,
Fraunhofer Heinrich Hertz Institute
Benjamin Bross Benjamin Bross
Project Manager,
Fraunhofer Heinrich Hertz Institute
Dan Grois Dan Grois
Former Senior Researcher,
Fraunhofer Heinrich Hertz Institute