Video: Case Study FIS Ski World Championship

Posted on by Russell Trafford-Jones

There’s a lot to learn when it comes to implementing video over IP, so it’s healthy to stand back from the details and see a working system in use to understand how the theory becomes reality. There’s been a clear change in the tone of conversation at the IP Showcase over the years as we’ve shifted from ‘trust us, this could work’ to ‘this is what it looks like!’ That’s not to say there’s not plenty to be done, but this talk about an uncompressed 2110 remote production workflow is great example of how the benefits of IP are being realised by broadcasters.
Robert Erickson is with Grass Valley specialising in sports such as the FIS Alpine World Ski Championships which were in the city of Åre in Sweden some 600km from Stockholm where Sweden’s public broadcaster SVT is based. With 80 cameras at the championships to be remotely controlled over an uncompressed network, this was no small project. Robert explains the two locations were linked by a backbone of two 100Gbps circuits.

The principle behind SVT’s project was to implement a system which could be redeployed, wouldn’t alter the viewers’ experience and would reduce staff and equipment on site. Interestingly the director wanted to be on-site meaning that the production was then split between much of the staff being in Stockholm, which of course was where most of the equipment was, and Åre. The cameras were natively IP, so no converters were needed in the field.

Centralisation was the name of the game, based in Stockholm, producing an end-to-end IP chain. Network switching was provided by Arista which aggregated the feeds of the cameras and brought them to Stockholm where the CCUs were located. Robert highlights the benefits of this approach which include the use of COTS switches, scalability and indifference as to the circuits in use. We then have a look inside the DirectIP connection which is a 10gig ‘pipe’ carrying 2022-6 camera and return feeds along with control and talkback, replicating the functionality of a SMPTE fibre in IP.

To finish up, Robert talks about the return visions, including multivewers, which were sent back to Åre. A Nimbra setup was used to take advantage of a lower-bandwidth circuit using JPEG 2000 to send the vision back. In addition, it carried the data to connect the vision mixer/switcher at Åre with the switch at Stockholm. This was the only point at which noticeable latency was introduced to the tune of around 4 frames.

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Speakers

Robert Erickson Robert Erickson
Strategic Account Manager Sports and Venues,
Grass Valley

Video: ATSC 3.0 Part IV – Advanced Emergency Alerting, AWARN & Interactive Content

Posted on by Russell Trafford-Jones

In a country where the weather can be life threatenin and where earthquakes and wild fires pose a real threat to life, an Early Alert System (EAS) is very important. This talk looks at the ‘Advanced Emergency Alerting’ system (AEA) that is available in ATSC 3.0 and the coalition behind it. It also talks about some of the interactive features possible.

Richard Chernock is back to dig deeper in to the set of standards which is known as ATSC 3.0. He starts by looking at the broadcaster’s role in being a public information provider both to first responders and to the public at large. ATSC 3.0 was seen as an opportunity to go much further than EAS available in ATSC 1.0. One improvement, as covered previously, allows for very robust transmission methods. AEA also provides rich media, version information and expiry information. Additionally it can be delivered to targeted areas.

The AWARN (Advance Warning and Response Network) is a project to look world-wide at the different EAS activities ongoing in order to bring learning into ATSC and represents both broadcasters and national agencies such as FEMA and homeland security. It provides practical advice on resilience (backup generator provision), how to maximise the verboseness of information, encryption and much more.

Finishing off this short talk, Richard highlights the OTT-style interactive services possible with ATSC 3.0. He shows a quiz format where the graphics are within the control of the broadcaster. Other examples discussed are interactive access to sports replays, purchasing merchandise, the ability to synchronise with a second screen and advert displays.
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Please note this is a 30 minute video but the version on YouTube repeats hence lasting 1.5 hours
Speakers

Richard Chernock Richard Chernock
Former CSO,
Triveni Digital

On-Demand Webinar: How to Prove Value with AI and Machine Learning

Posted on by Russell Trafford-Jones

This webinar is now available online.

We’ve seen AI entering our lives in many ways over the past few years and we know that this will continue. Artificial Intelligence and Machine Learning are techniques that are so widely applicable they will touch all aspects of our lives before too many more years have passed. So it’s natural for us to look at the broadcast industry and ask “How will AI help us?” We’ve already seen machine learning entering into codecs and video processing showing that up/downscaling can be done better by machine learning than with the traditional ‘static’ algorithms such as bicubic, lanczos and nearest neighbour. This webinar examines the other side of things; how can we use the data available within our supply chains and from our viewers to drive efficiencies and opportunities for better monetisation?

There isn’t a strong consensus on the difference between AI and Machine learning. One is that that Artificial Intelligence is a more broad term of smart computing. Others say that AI has a more real-time feedback mechanism compared to Machine Learning (ML). ML is the process of giving a large set of data to a computer and giving it some basic abilities so that it can learn for itself. A great example of this is the AI network monitoring services available that look at all the traffic flowing through your organisation and learn how people use it. It can then look for unusual activity and alert you. To do this without fixed thresholds (which for network use really wouldn’t work) is really not feasible for humans, but computers are up to that task.

For conversations such as this, it usually doesn’t matter how the computer achieves it, AI, ML or otherwise. The points how can you simplify content production? How can you get better insights into the data you have? How can you speed up manual tasks?

David Short from IET Media moderates this session with Steve Callanan who’s company WIREWAX is working to revolutionise video creation, asset management and interactive video services joined by Hanna Lukashevich from Fraunhofer IDMT (Institute for Digital Media Technology) who uses machine learning to understand and create music and sound. Grant Franklin Totten completes the panel with his experience at Al Jazeera who have been working on using AI in broadcast since 2018 as a way to help maintain editorial and creative compliance as well as detecting fake news and bias checking.

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Speakers

David Short Moderator: David Short
Vice Chair,
IET Media Technical Network
Steve Callanan Steve Callanan
Founder,
WIREWAX
Hanna Lukashevich Hanna Lukashevich
Head of Semantic Music Technologies,
Fraunhofer IDMT
Grant Franklin Totten Grant Franklin Totten
Head of Media & Emerging Platforms,
Al Jazeera Media Network

Video: OTT Fundamentals & hands-on video player lab

Posted on by Russell Trafford-Jones

Whilst there are plenty of videos explaining the basics streaming, few of them talk you through the basics of actually implementing a video player on your website. The principles taught in this hands-on Bitmovin webinar are transferable to many players, but importantly at the end of this talk you’ll have your own implementation of a video player which you can make in real time using their remix project at glitch.com which allows you to edit code and run it immediately in the browser to see your changes.

Ahead of the tutorial, the talk both explains the basics of compression and OTT led by Kieran Farr, Bitmovin’s VP of marketing and Andrea Fassina, Developer Evangelist. Andrea outlines a simplified OTT architecture where he looks at the ‘ingest’ stage which, in this example, is getting the videos from Instagram either via the API or manually. It then looks at the encoding step which compresses the input further and creates a range of different bitrates. Andrea explains that MPEG standards such as H.264, H.265 are commonly used to do this making the point that MPEG standards typically require royalty payments. This year, we are expecting to see VVC released by MPEG (H.266).

Andrea then explains the relationship between resolution, frame rate and file sizes. Clearly smaller files are better as they require less time to download leading to quicker downloads so faster startup times. Andrea discusses how the resolutions match the display resolutions with TVs having 1920×1080 resolution or 2160×3840 resolution. Given that higher resolutions have more picture detail, there is more information to be sent leading to larger file sizes.

Source: Bitmovin https://bit.ly/2VwStwC

When you come to set up your transcoder and player, there are a number of options you need to set. These are determined by these basics, so before launching into the code, Andrea looks further into the fundamental concepts. He next looks at video compression to explain the ways in which compression is achieved and the compromises within. Andrea starts from the first MJPEG codecs where each frame was its own JPEG image and they simply animated from one JPEG to another to show the video – not unlike animated GIFs used on the internet. However by treating each frame on its own ignores a lot of compression opportunity. When looking at one frame to the next, there are a lot of parts of the image which are the same or very similar. This allowed MPEG to step up their efforts and look across a number of frames to spot the similarities. This is typically referred to as temporal compression as is it uses time as part of the process.

In order to achieve this, MPEG splits all frames into blocks, squares in AVC, which are called macro blocks which be compared between frames. They then have 3 types of frame called ‘I’, ‘P’ and ‘B’ frames. The I frames have a complete description of that frame, similar to a JPEG photograph. P frames don’t have a complete description of the frame, rather they some blocks which have new information and some information saying that ‘this block is the same as this block in this other frame. B frames have no complete new image parts, but create the frame purely out of frames from the recent future and recent past; the B stands for ‘bi-directional’.

Ahead of launching into the code, we then look at the different video codecs available. He talks about AVC (discussed in detail here), HEVC (detailed in this talk) and compares the two. One difference is HEVC uses much more flexible macro block sizes. Whilst this increases computational complexity, it reduces the need to send redundant information so is an important part of the achieving the 50% bitrate reduction that HEVC typically shows over AVC. VP9 and AV1 complete the line-up as Andrea gives an overview of which platforms can support these different codecs.

Source: Bitmovin https://bit.ly/2VwStwC

Andrea then introduces the topic of Adaptive bitrate, ABR. This is vital in the effective delivery of video to the home or mobile phones where bandwidth varies over time. It requires creating several different renditions of your content at various bitrates, resolutions and even frame rate. Whilst these multiple encodes put a computational burden on the transcode stage, it’s not acceptable to allow a viewer’s player to go black, so it’s important to keep the low bitrate version. However there is a lot of work which can go into optimising the number and range of bitrates you choose.

Lastly we look at container formats such as MP4 used in both HLS and MPEG-DASH and is based on the file format ISO BMFF. Streaming MP4 is usually called fragmented MP4 (fMP4) as it is split up into chunks. Similarly MPEG2 Transport Streams (TS files) can be used as a wrapper around video and audio codecs. Andrea explains how the TS file is built up and the video, audio and other data such as captions are multiplexed together.

The last half of the video is the hands-on section during which Andrea talks us through how to implement a video player in realtime on the glitch project allowing you to follow along and do the same edits, seeing the results in your browser as you go. He explains how to create a list of source files, get the player working and styled correctly.

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Speakers

Kieran Farr Kieran Farr
VP of Marketing,
Bitmovin
Andrea Fassina Andrea Fassina
Developer Evangelist,
Bitmovin