Video: VVC – The new Versatile Video Coding standard

The Codec landscape is a more nuanced place than 5 years ago, but there will always be a place for a traditional Codec that cuts file sizes in half while harnessing recent increases in computation. Enter VVC (Versatile Video Codec) the successor to HEVC, created by MPEG and the ITU by JVET (Joint Video Experts Team), which delivers up to 50% compression improvement by evolving the HEVC toolset and adding new features.

In this video Virginie Drugeon from Panasonic takes us through VVC’s advances, its applications and performance in this IEEE BTS webinar. VVC aims not only to deliver better compression but has an emphasis on delivering at higher resolutions with HDR and as 10-bit video. It also acknowledges that natural video isn’t the only video used nowadays with much more content now including computer games and other computer-generated imagery. To achieve this, VVC has had to up its toolset.

 

 

Any codec is comprised of a whole set of tools that carry out different tasks. The amount that each of these tools is used to encode the video is controllable, to some extent, and is what gives rise to the different ‘profiles’, ‘levels’ and ‘tiers’ that are mentioned when dealing with MPEG codecs. These are necessary to allow for lower-powered decoding to be possible. Artificially constraining the capabilities of the encoder gives maximum performance guarantees for both the encoder and decoder which gives manufacturers control over the cost of their software and hardware products. Virginie walks us through many of these tools explaining what’s been improved.

Most codecs split the image up into blocks, not only MPEG codecs but the Chinese AVS codecs and AV1 also do. The more ways you have to do this, the better compression you can achieve but this adds more complexity to the encoding so each generation adds more options to balance compression against the extra computing power now available since the last codec. VVC allows rectangles rather than just squares to be used and the size of sections can now be 128×128 pixels, also covered in this Bitmovin video. This can be done separately for the chroma and luma channels.

Virginie explains that the encoding is done through predicting the next frame and sending the corrections on top of that. This means that the encoder needs to have a decoder within it so it can see what is decoded and understand the differences. Virginie explains there are three types of prediction. Intra prediction uses the current frame to predict the content of a block, inter prediction which uses other frames to predict video data and also a hybrid mode which uses both, new to VVC. There are now 93 directional intra prediction angles and the introduction of matrix-based intra prediction. This is an example of the beginning of the move to AI for codecs, a move which is seen as inevitable by The Broadcast Knowledge as we see more examples of how traditional mathematical algorithms are improved upon by AI, Machine Learning and/or Deep Learning. A good example of this is super-resolution. In this case, Virginie says that they used machine learning to generate some matrices which are used for the transform meaning that there’s no neural network within the codec, but that the matrices were created based on real-world data. It seems clear that as processing power increases, a neural network will be implemented in future codecs (whether MPEG or otherwise).

For screen encoding, we see that intra block copying (IBC) is still present from HEVC, explained here from 17:30 IBC allows part of a frame to be copied to another which is a great technique for computer-generated content. Whilst this was in HEVC it was not in the basic package of tools in HEVC meaning it was much less accessible as support in the decoders was often lacking. Two new tools are block differential pulse code modulation & transform skip with adapted residual coding each discussed, along with IBC in this free paper.

Virginie moves on to Coding performance explaining that the JVET reference software called VTM has been used to compare against HEVC’s HM reference and has shown, using PSNR, an average 41% improvement on luminance with screen content at 48%. Fraunhofer HHI’s VVenc software has been shown to be 49%.

Along with the ability to be applied to screen content and 360-degree video, the versatility in the title of the codec also refers to the different layers and tiers it has which stretch from 4:2:0 10 bit video all the way up to 4:4:4 video including spatial scalability. The main tier is intended for delivery applications and the high for contribution applications with framerates up to 960 fps, up from 300 in HEVC. There are levels defined all the way up to 8K. Virginie spends some time explaining NAL units which are in common with HEVC and AVC, explained here from slide 22 along with the VCL (Video Coding Layer) which Virginie also covers.

Random access has long been essential for linear broadcast video but now also streaming video. This is done with IDR (Instantaneous Decoding Refresh), CRA (Clean Random Access) and GDR (Gradual Decoding Refresh). IDR is well known already, but GDR is a new addition which seeks to smooth out the bitrate. With a traditional IBBPBBPBBI GOP structure, there will be a periodic peak in bitrate because the I frames are much larger than the B and, indeed, P frames. The idea with GDR is to have the I frame gradually transmitted over a number of frames spreading out the peak. This disadvantage is you need to wait longer until you have your full I frame available.

Virginie introduces subpictures which are a major development in VVC allowing separately encoded pictures within the same stream. Effectively creating a multiplexed stream, sections of the picture can be swapped out for other videos. For instance, if you wanted a picture in picture, you could swap the thumbnail video stream before the decoder meaning you only need one decoder for the whole picture. To do the same without VVC, you would need two decoders. Subpictures have found use in 360 video allowing reduced bitrate where only the part which is being watched is shown in high quality. By manipulating the bitstream at the sender end.

Before finishing by explaining that VVC can be carried by both MPEG’s ISO BMFF and MPEG2 Transport Streams, Virginie covers Reference Picture Resampling, also covered in this video from Seattle Video Tech allows reference frames of one resolution to be an I frame for another resolution stream. This has applications in adaptive streaming and spatial scalability. Virginie also covers the enhanced timing available with HRD

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Video is free to watch
Speaker

Virginie Drugeon Virginie Drugeon
Senior Engineer Digital TV Standardisation,
Panasonic

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 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: Benjamin Bross and Adam Wieckowski on Fraunhofer HHI, VVC, and Compression

VVC was finalised in mid-2020 after five years of work. AVC’s still going strong and is on its 26th version, so it’s clear there’s still plenty of work ahead for those involved in VVC. Heavily involved in AVC, HEVC and now VVC is the Fraunhofer Heinrich Hertz Institute (HHI) who are patent holders in all three and for VVC they are, for the first time, developing a free, open-source encoder and decoder for the standard.

In this video from OTTVerse.com, Editor Krishna Rao speaks to Benjamin Bross and Adam Więckowsk both from Fraunhofer HHI. Benjamin has previously been featured on The Broadcast Knowledge talking at Mile High Video about VVC which would be a great video to check out if you’re not familiar with this new codec given before its release.

They start by discussing how the institute is supported by the German government, money received from its patents and similar work as well as the companies who they carry out research for. One benefit of government involvement is that all the papers they produce are made free to access. Their funding model allows them the ability to research problems very deeply which has a number of benefits. Benjamin points out that their research into CABAC which is a very efficient, but complex entropy encoding technique. In fact, at the time they supported introducing it into AVC, which remember is 19 years old, it was very hard to find equipment that would use it and certainly no computers would. Fast forward to today and phones, computers and pretty much all encoders are able to take advantage of this technique to keep bitrates down so that ability to look ahead is beneficial now. Secondly, giving an example in VVC, Benjamin explains they looked at using machine learning to help optimise one of the tools. This was shown to be too difficult to implement but could be replaced by matrix multiplication which and was implemented this way. This matrix multiplication, he emphasises, wouldn’t have been able to be developed without having gone into the depths of this complex machine learning.

Krishna suggests there must be a lot of ‘push back’ from chip manufacturers, which Benjamin acknowledges though, he says people are just doing their jobs. It’s vitally important, he continues, for chip manufacturers to keep chip costs down or nothing would actually end up in real products. Whilst he says discussions can get quite heated, the point of the international standardisation process is to get the input at the beginning from all the industries so that the outcome is an efficient, implementable standard. Only by achieving that does everyone benefit for years to come.e

The conversation then moves on to the open source initiative developing VVenC and VVdeC. These are separate from the reference implementation VTM although the reference software has been used as the base for development. Adam and Benjamin explain that the idea of creating these free implementations is to create a standard software which any company can take to use in their own product. Reference implementations are not optimised for speed, unlike VVenC and VVdeC. Fraunhofer is expecting people to take this software and adapt it for, say 360-degree video, to suit their product. This is similar to x264 and x265 which are open source implementations of AVC and HEVC. Public participation is welcomed and has already been seen within the Github project.

Adam talks through a slide showing how newer versions of VVenC have increased speed and bitrate with more versions on their way. They talk about how some VVC features can’t really be seen from normal RD plots giving the example of open vs closed GOP encoding. Open GOP encoding can’t be used for ABR streaming, but with VVC that’s now a possibility and whilst it’s early days for anyone having put the new type of keyframes through their paces which enable this function, they expect to start seeing good results.

The conversation then moves on to encoding complexity and the potential to use video pre-processing to help the encoder. Benjamin points out that whilst there is an encode increase to get to the latest low bitrates, to get to the best HEVC can achieve, the encoding is actually quicker. Looking to the future, he says that some encoding tools scale linearly and some exponentially. He hopes to use machine learning to understand the video and help narrow down the ‘search space’ for certain tools as it’s the search space that is growing exponentially. If you can narrow that search significantly, using these techniques becomes practical. Lastly, they say the hope is to get VVenC and VVdeC into FFmpeg at which point a whole suite of powerful pre- and post- filters become available to everyone.

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Full transcript of the video
Speakers

Benjamin Bross Benjamin Bross
Head of Video Coding Systems Group,
Fraunhofer Heinrich Hertz Institute (HHI)
Adam Więckowski Adam Więckowski
Research Assistant
Fraunhofer HHI
Krishna Rao Vijayanagar Moderator: Krishna Rao Vijayanagar
Editor,
OTTVerse.com

Video: Deep Neural Networks for Video Coding

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