Encoding

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

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: Early Live Trials of VVC & EVC for OTT Delivery

Posted on by Russell Trafford-Jones

Much of 2020 was spent looking forward to the release of VVC, EVC and LC-EVC. A trio of MPEG standards fitting different use cases across this industry and beyond. Now they’ve all been released, it’s time to filter through finding which are the right fit for your workflows.

In this video, Thibaud Biatek from ATEME looks at using EVC and VVC for online streaming. EVC, is the Essential Video Codec, and VVC stands for the Versatile Video Codec. If you’d like to know more about the codecs themselves, check out this video talking about all of them. The driver for new codecs highlighted in the video is that internet traffic is over 70% video. But taking a step back, we need to remember that these codecs all come delivering more than just compression savings. Some, like LCEVC bring easier compression on embedded systems and easier decoding for AI applications. VVC represents the state of the art in compression techniques and EVC offers a totally royalty-free encoding option which is missing from all other MPEG codecs.

MPEG are very open that VVC is the same fundamental design as MPEG 2 was, it’s the techniques in each functional block which have improved in both quantity and ability that marks the difference. As such, Thibaud notes that you can create the same base code for an EVC codec as for VVC, thus you only need one software library to deliver an encode for both codecs. If you look at partitioning the screen into blocks, we see that VVC does everything EVC does but ads the ability to have diagonals. Screen Content Coding (SCC) is a speciality of VVC which adds it as a standard capability, unlike HEVC which had it optional. EVC also has SCC but only contains Intra Block Copy to implement it; VVC has three more on top of IBC.

Thibaud outlines how ATEME have done their initial implementations of VVC and EVC. Though they are not yet full implementations, they are seeing notable improvements over HEVC, particularly for VVC’s encoding of 8K which is attributed to the larger block sizes allowed in partitioning. He then takes us through the trials to date which have involved UHD VVC over satellite to the current test which is a real-time VVC encode to a CMAF ladder of 720p, 1080p and 2160p. In partnership with Akamai, this was then distributed as CMAF to the end-user which was using IETR’s openVVC decoder.

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Speaker

Thibaud Biatek Thibaud Biatek
Reasearch & Innovation Engineer
ATEME