If we ever had a time when most displays were the same resolution, those days are long gone with smartphone and tablets with extremely high pixel density nestled in with laptop screens of various resolutions and 1080-line TVs which are gradually being replaced with UHD variants. This means that HD videos are nearly always being upscaled which makes ‘getting upscaling right’ a really worthwhile topic. The well-known basic up/downscaling algorithms have been around for a while, and even the best-performing Lanczos is well over 20 years old. The ‘new kid on the block’ isn’t another algorithm, it’s a whole technique of inferring better upscaling using machine learning called ‘super resolution’.
Nick Chadwick from Mux has been running the code and the numbers to see how well super resolution works. Taking to the stage at Demuxed SF, he starts by looking at where scaling is used and what type it is. The most common algorithms are nearest neighbour, bi-cubic, bi-linear and lanczos with nearest neighbour being the most basic and least-well performing. Nick shows, using VMAF that using these for up and downscaling, that the traditional opinions of how well these algorithms perform are valid. He then introduces some test videos which are designed to let you see whether your video path is using bi-linear or bi-cubic upscaling, presenting his results of when bi-cubic can be seen (Safari on a MacBook Pro) as opposed to bi-linear (Chrome on a MacBook Pro). The test videos are available here.
In the next part of the talk, Nick digs a little deeper into how super resolution works and how he tested ffmpeg’s implementation of super resolution. Though he hit some difficulties in using this young filter, he is able to present some videos and shows that they are, indeed, “better to view” meaning that the text looks sharper and is easier to see with details being more easy pick out. It’s certainly possible to see some extra speckling introduced by the process, but VMAF score is around 10 points higher matching with the subjective experience.
The downsides are a very significant increase in computational power needed which limits its use in live applications plus there is a need for good, if not very good, understanding of ML concepts and coding. And, of course, it wouldn’t be the online streaming community if clients weren’t already being developed to do super-resolution on the decode despite most devices not being practically capable of it. So Nick finishes off his talk discussing what’s in progress and papers relating to the implementation of super resolution and what it can borrow from other developing technologies.
HbbTV is a transmission standard seeking to unify over-the-air transmissions and broadband-delivered services into a seamless service. This aim is the same as ATSC 3.0 though the means of achieving it are not the same. HbbTV has seen growing use since its debut in 2006 and is now on its 2nd iteration.
James starts by looking at the boxes available and, importantly, the SDKs behind them which are many and varied. We’re then taken through some of the XML necessary to get an HbbTV app up and running including some key “dos and don’ts”. Now that we’ve seen how to invoke a program, James takes us on an amusing journey into getting a loading spinner to work and the many attempts, all highly reasonable, to convince this to be rendered.
For a video service, playing videos is probably the most important function so next we look at the surprising number of ways one can fail to get video to play using quite rational parameters in MPEG DASH seeing the ways to make it work and the ones to avoid. Finally we see the variety of responses that boxes report back their failure to render video.
A cautionary tale, or a guide to survive? You decide. Watch now!
Measuring video quality is done daily around the world between two video assets. But what happens when you want to take the aggregate quality of a whole manifest? With VMAF being a well regarded metric, how can we use that in an automatic way to get the overview we need?
In this talk, Nick Chadwick from Mux shares the examples and scripts he’s been using to analyse videos. Starting with an example where everything is equal other than quality, he explains the difficulties in choosing the ‘better’ option when the variables are much less correlated. For instance, Nick also examines the situations where a video is clearly better, but where the benefit is outweighed by the minimal quality benefit and the disproportionately high bitrate requirement.
So with all of this complexity, it feels like comparing manifests may be a complexity too far, particularly where one manifest has 5 renditions, the other only 4. The question being, how do you create an aggregate video quality metric and determine whether that missing rendition is a detriment or a benefit?
Before unveiling the final solution, Nick makes the point of looking at how people are going to be using the service. Depending on the demographic and the devices people tend to use for that service, you will find different consumption ratios for the various parts of the ABR ladder. For instance, some services may see very high usage on 2nd screens which, in this case, may take low-resolution video and also lot of ‘TV’ size renditions at 1080p50 or above with little in between. Similarly other services may seldom ever see the highest resolutions being used, percentage-wise. This shows us that it’s important not only to look at the quality of each rendition but how likely it is to be seen.
To bring these thoughts together into a coherent conclusion, Nick unveils an open-source analyser which takes into account not only the VMAF score and the resolution but also the likely viewership such that we can now start to compare, for a given service, the relative merits of different ABR ladders.
The talk ends with Nick answering questions on the tendency to see jumps between different resolutions – for instance if we over-optimise and only have two renditions, it would be easy to see the switch – how to compare videos of different resolutions and also on his example user data.
Open source software can be found powering streaming solutions everywhere. Veterans of the industry on this panel at Streaming Media West, give us their views on how to successfully use open source in on-air projects whilst minimising risk.
The Streaming Video Alliance’s Jason Thibeault starts by finding out how much the panelists and their companies use open source in their work and expands upon that to ask how much the support model matters. After all, some projects have paid support but based on free software whereas others have free community-provided support. The feeling is that it really depends on the community; is it large and is it active? Not least of the considerations is that, in a corporate setting, if the community is quick to accuse, is it right to ask your staff to go through layers of ‘your a newbie’ and other types of pushback each time they need to get an answer?
Another key question is whether we give should back to the open source community and, if so, how. The panels discusses the difficulties in contributing code but also covers the importance of other ways of contributing – particularly when the maintainer is one individual. Contribution of money is an obvious, but often forgotten way to help but writing documentation is also really helpful as is contributing on the support forums. This all makes for a vibrant community and increases the chances that other companies will adopt the project into their workflows…which then makes the community all the stronger.
With turn-key proprietary solutions ready to to be deployed, Jason asks whether open source actually saves money on the occasions that you can, indeed, find a proprietary solution that fits your requirements.
Lastly, the panel talks about the difficulty in balancing adherence to the standards compared with the speed at which open source communities can move. They can easily deliver the full extent of the standard to date and then move on to fixing the remaining problems so far not addressed by the developing standard. Whilst this is good, they risk implementing in ways which may cause issues in the future when the standard finally catches up.
The panel session finishes with questions from the audience.