AVC, now 16 years old, is long in the tooth but supported by billions of devices. The impetus to replace it comes from the drive to serve customers with a lower cost/base and a more capable platform. Cue the new contenders VVC and AV1 – not to mention HEVC. It’s no surprise they comptes better then AVC (also known as MPEG 4 and h.264) but do they deliver a cost efficient, legally safe codec on which to build a business?
Thierry Fautier has done the measurements and presents them in this talk. Thierry explains that the tests were done using reference code which, though unoptimised for speed, should represent the best quality possible from each codec and compared 1080p video all of which is reproduced in the IBC conference paper.
Licensing is one important topic as, by some, HEVC is seen as a failed codec not in terms of its compression but rather in the réticente by many companies to deploy it which has been due to the business risk of uncertain licensing costs and/or the expense of the known licensing costs. VVC faces the challenge of entering the market and avoiding these concerns which MPEG is determined to do.
Thierry concludes by comparing AVC against HEVC, AV1 and VVC in terms of deployment dates, deployed devices and the deployment environment. He looks at the challenge of moving large video libraries over to high-complexity codecs due to cost and time required to re-compress. The session ends with questions from the audience. Watch now! Speaker
President-Chair at Ultra HD Forum,
VP Video Strategy, Harmonic
Per-title encoding is a common method of optimising quality and compression by changing the encoding options on a file-by-file basis. Although some would say the start of per-scene encoding is the death knell for per-title encoding, either is much better than the more traditional plan of applying exactly the same settings to each video.
This talk with Mux’s Nick Chadwick and Ben Dodson looks at what per-title encoding is and how to go about doing it. The initial work involves doing many encodes of the same video and analysing each for quality. This allows you to out which resolutions and bitrates to encode at and how to deliver the best vide.
Ben Dodson explains the way they implemented this at Mux using machine learning. This was done by getting computers to ‘watch’ videos and extract metadata. That metadata can then be used to inform the encoding parameters without the computer watching the whole of a new video.
Nick takes some time to explain MUX’s ‘convex hulls’ which give a shape to the content’s performance at different bitrates and helps visualise the optimum encoding parameters the content. Moreover we see that using this technique, we can explore how to change resolution to create the best encode. This doesn’t always mean reducing the resolution; there are some surprising circumstances when it makes sense to start at high resolutions, even for low bitrates.
The next stage after per-title encoding is to segment the video and encode each segment differently which Nick explores and explains how to deliver different resolutions throughout the stream seamlessly switching between them. Ben takes over and explains how this can be implemented and how to chose the segment boundaries correctly, again, using a machine learning approach to analysis and decision making.
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.
We’ve got used to a world of near-universal AVC/h.264 support, but in our desire to deliver better services, we need new codecs. VVC is nearing completion and is attracting increasing attention with its ability to deliver better compression than HEVC in a range of different situations.
Benjamin Bross from the Fraunhofer Institute talks at Mile High Video 2019 about what Versatile Video Coding (VVC) is and the different ways it achieves these results. Benjamin starts by introducing the codec, teasing us with details of machine learning which is used for block prediction and then explains the targets for the video codec.
Next we look at the bitrate curves showing how encoding has improved over the years and where we can expect VVC to fit in before showing results of testing the codec as it exists today which already shows improvement in compression. Encoding complexity and speed are also compared and as expected complexity has increased and speed has reduced. This is always a challenge at the beginning of a new codec standard, but is typically solved in due course. Benjamin also looks at the effect of resolution and frame rate on compression efficiency.
Every codec has sets of tools which can be tuned and used in certain combinations to deal with different types of content so as to optimise performance. VVC is no exception and Benjamin looks at some of the highlights:
Screen Content Coding – specific tools to encode computer graphics rather than ‘natural’ video. With the sharp edges on computer screens, different techniques can produce better results
Reference Picture Rescaling – allows resolution changes in the video stream. This can also be used to deliver multiple resolutions at the same time
Independent Sub Pictures – separate pictures available in same raster. Allows, for instance, sending large resolutions and allowing decoders to only decode part of the picture.