Video: Subjective and Objective Quality Assessment

Video quality is a key part of user experience, so understanding how different parts of your distribution chain can affect your video in different ways is an important factor ensuring continued quality in the service and quick fault finding where problems are reported.

Abdul Rehman from SSIMWAVE speaks at the Kitchener-Warterloo Video Technology Meetup explaining both subjective quality assessment where humans judge the quality of the video and objective quality assessments where computers analyse, often terabytes, of video to assess the quality.

Starting with a video showing examples of different problems that can occur in the chain, Abdul explains how many things can go wrong including lost or delayed data, incorrect content and service configuration checks. Display devices, nowadays, come in many shapes, sizes and resolutions which can, in turn, cause impairments with display as can the player and viewing conditions. These are only around half of the different possibilities which include the type of person – a golden eye, or a pure consumer.

In order to test your system, you may need test codecs and you will need test content. Abdul talks about subject rated databases which have images which have certain types of distortions/impairments. After seeing many examples of problem images, Abdul asks the question of who to deal with natural images which look similar or deliberate use, for creative purposes, of distorted videos.

Subjective video quality assessment is one solution to this since it uses people who are much better at detecting creative quality than computers. As such, this avoids many false positives where video may be judged as bad, but there is intent in the use. Moreover, it also represents direct feedback from your target group. Abdul talks through the different aspects of what you need to control for when using subjective video quality assessment in order to maximise its usefulness and allow results from different sessions and experiments to be directly compared.

This is to be compared against objective video quality assessment where a computer is harnessed to plough through the videos. This can be very effective for many applications meaning it can shine in terms of throughput and number of measurements. Additionally, it can make regression testing very easy. The negatives can be cost, false positives and sometimes speed – depending on the application. You then can take your pick of algorithms such as MS-SSIM, VMAF and others. Abdul finishes by explaining more about the benefits and what to look out for.

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Speakers

Abdul Rehman Abdul Rehman
Cofounder, CEO and CTO,
SSIMWAVE

Video: Super Resolution – The scaler of tomorrow, here today!

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.

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Speaker

Nick Chadwick Nick Chadwick
Software Engineer,
Mux

Video: Hardware Transcoding Solutions For The Cloud

Hardware encoding is more pervasive with Intel’s Quick Sync embedding CUDA GPUs inside GPUs plus NVIDIA GPUs have MPEG NVENC encoding support so how does it compare with software encoding? For HEVC, can Xilinx’s FPGA solution be a boost in terms of quality or cost compared to software encoding?

Jan Ozer has stepped up to the plate to put this all to the test analysing how many real-time encodes are possible on various cloud computing instances, the cost implications and the quality of the output. Jan’s analytical and systematic approach brings us data rather than anecdotes giving confidence in the outcomes and the ability to test it for yourself.

Over and above these elements, Jan also looks at the bit rate stability of the encodes which can be important for systems which are sensitive to variations such services running at scale. We see that the hardware AVC solutions perform better than x264.

Jan takes us through the way he set up these tests whilst sharing the relevant ffmpeg commands. Finally he shares BD plots and example images which exemplify the differences between the codecs.

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Download the slides
Speaker

Jan Ozer Jan Ozer
Principal, Streaming Learning Center
Contributing Editor, Streaming Media

Video: Speed-Distortion Optimization: Tradeoffs in Open Source HEVC Encoding

HEVC, also known as h.265, has been with us for 7 years and whilst its use continues to grow, its penetration remains low in streaming and broadcast transmissions. One reason for this is the increase in compute power it requires. With 4-rung ABR ladder for streaming being so common, a two-fold increase in complexity means finding 8 times as much compute power in your encoder.

This talk, led by MulticoreWare and Comcast, discusses the x.265 codec and the abilities of the presets. Pradeep Ramachandran uses a diagram of the x.265 encode system to expose some of the ways in which x.264 works.

Pradeep then gives an overview of the key tools of HEVC ahead of explaining those they tested against using UHD HDR content. Alex Giladi then takes the stage detailing their use of Dynamically Controlled RDO and how they were able to determine the best combination of modes to create the best encode.

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Speakers

Pradeep Ramachandran Pradeep Ramachandran
Principal Engineer in Office of CTO,
MulticoreWare
Alex Giladi Alex Giladi
Distinguished Engineer,
Comcast