Video: Per-Title Encoding in the Wild

How deep do you want to go to make sure viewers get the absolute best quality streamed video? It’s been common over the past few years not to just choose 7 bitrates for a streamed service and encode everything to those bitrates. Rather to at least vary the bitrate for each video. In this talk we examine why doing this is leaving bitrate savings on the table which, in turn, means bitrate savings for your viewers, faster time-to-play and an overall better experience.

Jan Ozer starts with a look at the evolution of bitrate optimisation. It started with Beamr and, everyone’s favourite, FFmpeg. Both of which re-encode every frame until they get the best quality. FFmpeg’s CRF mode will change the quantizer parameter for each frame to maintain the same quality throughout the whole file, though with a variable bitrate. Beamr would encode each frame repeatedly reducing the bitrate until it got the desired quality. These worked well but missed out on a big trick…

Over the years, it’s been clear that sometimes 720p at 1Mbps looks better than 1080p at 1Mbps. This isn’t always the case and depends on the source footage. Much rolling news will be different from premium sports content in terms of sharpness and temporal content. So, really, the resolution needs to be assessed alongside data rate. This idea was brought into Netflix’s idea of per-title encoding. By re-encoding a title hundreds of times with different resolutions and data rates, they were able to determine the ‘convex hull’ which is a graph showing the optimum balance between quality, bitrate and resolution. That was back in 2015. Moving beyond that, we’ve started to consider more factors.

The next evolution is fairly obvious really, and that’s to make these evaluations not for each video, but for each shot. Doing this, Jan explains, offers bitrate improvements of 28% for AVC and more for other codecs. This is more complex than per-title because the stream itself changes, for instance, GOP sizes, so whilst we know this is something Netflix is using, there are no available commercial implementations currently.

Pushing these ideas further, perhaps the streaming service should take in to account the device on which you are viewing. Some TV’s typically only ever take the top two rungs on the ladder, yet many mobile devices have low-resolutions screens and never get around to pulling the higher bitrates. So profiling a device based on either its model or historic activity can allow you to offer different ABR ladders to allow for a better experience.

All of this needs to be enabled by automatic, objective metrics so the metrics need to look out for the right aspects of the video. Jan explains that PSNR and MS-SSIM, though tried and trusted in the industry, only measure spatial information. Jan gives an overview of the alternatives. VMAF, he says, ads a detail loss metric, but it’s not until we start using PW-SSIM fro Bright cove where aspects such as device information is taken into account. SSIMPLUS does this and also considers wide colour gamut HDR and frame rates. Similarly ATEME’s ‘Quality Vector’ considers frame rate and HDR.

Dr. Abdul Rehman follows Jan with his introduction to SSIMWAVE’s technologies and focuses on their ability to understand what quality the viewer will see. This allows a provider to choose whether to deliver a quality of ’70’ or, say, ’80’. Each service is different and the demographics will expect different things. It’s important to meet viewer expectation to avoid churn, but it’s in everyone’s interest to keep the data rate as low as possible.

Abdul gives the example of banding which is something that is not easily picked up by many metrics and so can be introduced as the encode optimiser continues to reduce the bitrate oblivious to the obvious banding. He says that since SSIMPLUS is not referenced to a source, this can give an accurate viewer score no matter the source material. Remember that if you use PSNR, you are comparing against your source. If the source is poor, your PSNR score might end up close to the maximum. The trouble is, your viewers will still see the poor video you send them, not caring if this is due to encoding or a bad source.

The video ends with a Q&A.

Watch now!
Speakers

Jan Ozer Jan Ozer
Principal, Stremaing Learning Center
Contributing Editor, Streaming Media
Abdul Rehman Abdul Rehman
CEO,
SSIMMWAVE

Video: Banding Impairment Detection

It’s one of the most common visual artefacts affecting both video and images. The scourge of the beautiful sunset and the enemy of natural skin tones, banding is very noticeable as it’s not seen in nature. Banding happens when there is not enough bit depth to allow for a smooth gradient of colour or brightness which leads to strips of one shade and an abrupt change to a strip of the next, clearly different, shade.

In this Video Tech talk, SSIMWAVE’s Dr. Hojat Yeganeh explains what can be done to reduce or eliminate banding. He starts by explaining how banding is created during compression, where the quantiser has reduced the accuracy of otherwise unique pixels to very similar numbers leaving them looking the same.

Dr. Hojat explains why we see these edges so clearly. By both looking at how contrast is defined but also by referencing Dolby’s famous graph showing contrast steps against luminance where they plotted 10-bit HDR against 12-bit HDR and show that the 12-bit PQ image is always below the ‘Barten limit’ which is the threshold beyond which no contrast steps are visible. It shows that a 10-bit HDR image is always susceptible to showing quantised, i.e. banded, steps.

Why do we deliver 10-bit HDR video if it can still show banding? This is because in real footage, camera noise and film grain serve to break up the bands. Dr. Hojat explains that this random noise amounts to ‘dithering’. Well known in both audio and video, when you add random noise which changes over time, humans stop being able to see the bands. TV manufacturers also apply dithering to the picture before showing which can further break up banding, at the cost of more noise on the image.

How can you automatically detect banding? We hear that typical metrics like VMAF and SSIM aren’t usefully sensitive to banding. SSIMWAVE’s SSIMPLUS metric, on the other hand, has been created to also be able to create a banding detection map which helps with the automatic identification of banding.

The video finishes with questions including when banding is part of artistic intention, types of metrics not identifiable by typical metrics, consumer display limitations among others.

Watch now!
Speakers

Dr. Hojat Yeganeh Dr. Hojat Yeganeh
Senior Member Technical Staff,
SSIMWAVE Inc.

Video: No-Reference QoE Assessment: Knowledge-based vs. Learning-based

Automatic assessment of video quality is essential for creating encoders, selecting vendors, choosing operating points and, for online streaming services, in ongoing service improvement. But getting a computer to understand what looks good and what looks bad to humans is not trivial. When the computer doesn’t have the source video to compare against, it’s even harder.

In this talk, Dr. Ahmed Badr from SSIMWAVE looks at how video quality assessment (VQA) works and goes into detail on No-Reference (NR) techniques. He starts by stating the case for VQA which is an extension, and often replacement for subjective scoring by people. Clearly this is time-consuming, can be more expensive due to involvement of people (and the time) plus requires specific viewing conditions. When done well, a whole, carefully decorated room is required. So when it comes to analysing all the video created by a TV station or automating per-title encoding optimisation, we know we have to remove the human element.

Ahmed moves on to discuss the challenges of No Reference VQA such as identifying intended blur or noise. NR VQA is a two-step process with the first being extracting features from the video. These features are then mapped to a quality model which can be done with a machine learning/AI process which is the technique which Ahmed analyses next. The first task is to come up with a dataset of videos which should be carefully chosen, then it’s important to choose a metric to use for the training, for instance, MS-SSIM or VMAF. This is needed so that the learning algorithm can get the feedback it needs to improve. The last two elements are choosing what you are optimising for, technically called a loss function, and then choosing an AI model for use.

The data set you create needs to be aimed at exploring a certain aspect or range of aspects of video. It could be that you want to optimise for sports, but if you need a broad array of genres, optimising for reducing compression or scaling artefacts may be the main theme of the video dataset. Ahmed talks about the millions of video samples that they have collated and how they’ve used that to create their metric called SSIMPLUS which can work both with a reference and without.

Watch now!
Speaker

Dr. Ahmed Badr Dr. Ahmed Badr
SSIMWAVE

Video: AV1 vs. HEVC: Perceptual Evaluation of Video Encoders

Zhou Wang explains how to compare HEVC & AVC with AV1 and shares his findings. Using various metrics such as VMAF, PSNR and SSIMPlus he explores the affects of resolution on bitrate savings and then turns his gaze to computation complexity.

This talk was given at the Mile High Video conference in Denver CO, 2018.

Speakers

Zhou Wang Zhou Wang
Chief Science Officer,
SSIMWAVE Inc.