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.

Watch now!
Speakers

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

Video: Winner takes all: Unlocking the opportunity in video games and esports.

Posted on by Russell Trafford-Jones

Even without the pandemic, esports was set to continue its growth over 2020. By the end of 2020, esports had had quite a boost while other sports were canceled. And whilst esports is a large market, it’s still often misunderstood by those unfamiliar with it. This panel recently looked at not only how Covid had changed esports but also how traditional broadcasters can engage with this popular entertainment segment.

The session starts with an overview of the Asian esports market with Daniel Ahmad from Niko Partners. In 2019 there were 1.3 billion gamers in the whole market. In China, there were 321 million PC gamers who spent around $14.6 billion, plus a mobile gaming population which, by 2024, will have doubled their spending to $32 billion across 737 million gamers.

With esports clearly on the rise, the Sports Video Group’s Jason Dachman has brought some of the key players in esports together, Anna Lockwood from Telstra, Steven Jalicy from ESL, David Harris from Guinevere Capital and Yash Patel from Telstra Ventures. Straight off the bat, they tackle the misconceptions that mainstream media has regarding esports. Steven from ESL says people are quick to dismiss the need for quality in esports. In some ways, the quality needs, he says, are more demanding. David Harris says that people overstate esports’ size today and underestimate how big it will be in the future. Anna Lockwood on the other hand sees that people don’t realise how different and powerful the stories told in esports are.
 

 
Asked to talk about how Covid changed ESL’s plans in 2020, he explained that at the final count, they had actually done more events than last year. ESL had already switched to remote working for much of the technical roles in 2018, at the time seen as quite a forward-thinking idea. Covid forced the rest of the workflows to change as stadium appearances were canceled and gamers competed remotely. Fortunately, the nature of esports makes it relatively easy to move the players. Post-Covid, Steven says that arenas will be back as they are very popular and an obvious focus for tournaments. Seeing players in the flesh is an important part of being a fan. But much of the technical changes, are likely to stay at least in part.

Jason Cacheman asks the panel why esports on linear TV hasn’t been very successful. Many of the panelists agree that the core fans simply aren’t that interested in watching on linear TV as they already have a set up to watch streamed which suits them, often, much better. After a question from the audience, their suggestions for incorporating linear TV into esports is to acknowledge that you’re talking to a group of people who are interested but really don’t know, possibly, anything at all. Linear TV is a great place for documentaries and magazine shows which can educate the audience about the different aspects of esports and help them relate. For instance, a FIFA or NBA esports tournament is easier to understand than a Magic: The Gathering or League of Legends tournament. Linear TV can also spend time focussing on the many stories that are involved in esports both in-game and out. Lastly, esports can be a conduit for traditional broadcasters to bring people onto their digital offerings. As an example, the BBC have an online-only channel, BBC Three. By linking esports content on both BBC Two and BBC Three, they can get interested viewers of their broadcast channel to take an interest in their online channel and also have the potential to appeal to core esports fans using their digital-only channel.

Other questions from the audience included the panel’s opinion on VR in esports, use of AI, how to start working in esports, whether it’s easier to bring esports engineers into broadcast or the other way round. The session finished with a look ahead to the rest of 2021. The thoughts included the introduction of bargaining agreements, salary caps, more APIs for data exchange, and that what we saw in 2020 was a knee-jerk reaction to a new problem; 2021 will see real innovation around staying remote and improving streams for producers and, most importantly, the fans.

Watch now!
Speakers

David Harris David Harris
Managing Director,
Guinevere Capital
Steven Jalicy Steven Jalicy
Global Head of Streaming,
ESL Gaming
Anna Lockwood Anna Lockwood
Head of Global Sales,
Telstra Broadcast Services
Yash Patel Yash Patel
General Partner,
Telstra Ventures
Jason Dachman Moderator: Jason Dachman
Chief Editor,
Sports Video Group

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.

Watch now!
Speaker

Sam Bhattacharyya Sam Bhattacharyya
CEO, Co-founder
Vectorly

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.

Watch now!
Speaker

Dan Grois Dan Grois
Principal Researcher,
Comcast