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.
Per-title encoding with machine learning is the topic of this video from MUX.
Nick Chadwick explains that rather than using the same set of parameters to encode every video, the smart money is to find the best balance of bitrate and resolution for each video. By analysing a large number of combinations of bitrate and resolution, Nick shows you can build what he calls a ‘convex hull’ when graphing against quality. This allows you to find the optimal settings.
Doing this en mass is difficult, and Nick spends some time looking at the different ways of implementing it. In the end, Nick and data scientist Ben Dodson built a system which optimses bitrate for each title using neural nets trained on data sets. This resulted in 84% of videos looking better using this method rather than a static ladder.
Optimising encoding by per-title encoding is very common nowadays, though per-scene is slowly pushing it aside. But with so many companies offering per-title encoding, how do we determine which way to turn?
Jan Ozer experimented with them, so we didn’t have to. Jan starts by explaining the principles of per-title encoding and giving an overview of the market. He then explains some of the ways in which it works including the importance of changing resolution as much as changing
As well as discussing the results, with Bitmovin being the winner, Jan explains ‘Capped CRF’ – how it works, how it differs from CBR & VBR and why it’s good.
Finally, we are left with some questions to ask when searching for our own per-title technology to solve the problem we have such as “can it adjust rung resolutions?”, “Can you apply traditional data rate controls?” amongst others.
MUX is a very pro-active company pushing forward streaming technology. At NAB 2019 they have announced Audience Adaptive Encoding which is offers encodes tailored to both your content but also the typical bitrate of your viewing demographic. Underpinning this technology is machine learning and their Per-title encoding technology which was released last year.
This talk with Nick Chadwick looks at what per-title encoding is, how you can work out which resolutions and bitrates to encode at and how to deliver this as a useful product.
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 see some surprising circumstances when it makes sense to start at high resolutions, even for low bitrates.
Looking then at how to actually work out on a title-by-title basis, Nick explains the pros and cons of the different approaches going on to explain how MUX used machine learning to generate the model they created to make this work.
Finishing off with an extensive Q&A, this talk is a great overview on how to pick great encoding parameters, manually or otherwise.
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