The P.1203 video quality metric for HTTP Adaptive Streaming
A highly accurate model for measuring the quality of streaming sessions — and an international standard
ITU-T Rec. P.1203 calculates the Quality of Experience of HTTP Adaptive Streaming sessions. It covers all degradations that may occur in a video stream caused by lossy compression, temporal or spatial downscaling, and stalling effects due to rebuffering events — including initial loading (startup time).
The standard predicts the QoE in terms of Mean Opinion Scores (MOS) on a scale from 1–5, where 1 refers to Bad quality, and 5 to Excellent.
The models have been trained and validated on over 1,000 audiovisual sequences that were rated by human viewers with over 25,000 individual ratings.
The models described in the standard have been created by an international consortium of academic and industrial partners.
P.1203 takes complex inputs and delivers an easy to understand Video MOS.
Several modules for different aspects of the quality estimation
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The input streams are analyzed separately for audio and video quality. The P.1203.1 and P.1203.2 Pv and Pa modules produce a per-one-second MOS value corresponding to the per-stream video and audio quality, which are then integrated over time — considering any influence by stalling and quality fluctuation happening during playout. The integration happens in the Pq module. It predicts a final MOS value.
This MOS value corresponds to the quality rating a user would have given had she/he seen the video.
A unique feature of the model is the temporal integration done by the Quality Integration module (Pq), which takes into account effects like initial loading delay, stalling, and quality fluctuation over time. This accurately models subjects’ perception of an entire streaming session, especially in comparison to other video-quality only models (e.g., VMAF).
The modular structure allows the integration module to be used with other video/audio quality models, under the condition that the combination is validated in terms of prediction accuracy. For instance, video quality models from the ITU-T P.1204 family of standards can be used together with P.1203.
Modular approach (above) and temporal intergration (below)
You don’t have to check every pixel.
Bitstream-based metrics are less CPU-intensive than full-reference metrics.
Metadata & bitstream-based approach
Efficient and accurate — less data and computational resources needed.
The P.1203 standard is a distinct departure from conventional metrics like PSNR, SSIM, and VMAF. Unlike these full-reference metrics, which rely on source files and received files being available in decoded form, P.1203 takes a metadata and bitstream-based approach, analyzing the stream metadata (codec, bitrate, resolution, …), frame types and sizes, or the encoded bitstream file — without the need for decoding or referencing the original source.
This has huge benefits in terms of required computational resources and data storage. Also, DRM is not an issue for the model when operated in the metadata- or frame-based mode.
Four modes of operation for differnt levels of data
Four modes based on the level of information
Adjust to the computational resources at hand.
P.1203’s simplest mode of operation (mode 0) takes as input: audio/video bitrate, video resolution, frames per second, and stalling events happening at the client side. Depending on the available data, it offers higher modes of operation that increase prediction accuracy at the expense of being (somewhat) more computationally intensive and requiring input data from more in-depth bitstream inspection.
While Mode 0 has access to basic data, Mode 1 can inspect the packet headers of the transmitted stream to obtain frame sizes and types. This works with DRM-protected content and is a great choice for client-side evaluation where bitstreams cannot be read directly (e.g., due to encryption).
Modes 2 and 3 have access to the bitstream itself, where mode 2 only accesses 2% of the stream to reduce computing efforts. Mode 2 is rarely used in practice, since Mode 3 can be calculated rather efficiently using modern hardware.
MOS model to real MOS correlation using P.1203
The ITU-T Rec. P.1203 model offers excellent performance when compared against subjective data. With a correlation of up to 0.9, real users’ ratings can be predicted with great accuracy.
Source: Robitza et al., MMSys 2018
Highly accurate Quality of Experience predictions
Validated with more than 1,000 video sequences and 25,000 individual ratings.
The MOS reflects the overall experience of the user. It includes effects of initial loading, stalling, and quality variations throughout the video. This is the primary value of concern when assessing the streaming quality.
The value range can be interpreted as follows:
In practice, a perfect score of 5 cannot be reached, since even humans cannot always agree that a video sequence is Excellent. This means that the highest possible score will be around 4.7. Any value above 4 is therefore considered very good.
A value between 3 and 4 indicates issues with the streaming performance. A value below 3 indicates severe problems with the streaming performance. Of course, this is still dependent on the actual setup and there is never an “absolute” MOS.
The MOS output of the ITU-T model corresponds very closely to the user ratings, with a correlation of about 0.85 to 0.9. This is highly accurate, especially considering the multitude of factors that may influence a final rating.
A single number makes it possible to know where you stand against competitors.
With the MOS, it is easy to detect regions or times of bad service quality, without having to rely on complex technical indicators.
Use the underlying diagnostic KPIs to perform a root cause analysis and improve your service.
Identify customer experience and customer satisfaction issues. And solve them — for happy customers.
Want to take your video quality monitoring a step further?
The products that power the QoS and QoE monitoring solutions.
Our core technology to drive video and web tests. Any service that runs in a browser — we can measure it.
Automated measurements – at scale. A powerful automation framework for running Surfmeter measurements on a schedule.
More than just data – our streaming analytics dashboard combines all Surfmeter measurement results.
Let us know your questions regarding video MOS, what models perform best and how to integrate MOS calculation into your streaming setup.
We will find the solution that works best for you.
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We constantly implement new data sources and visualisation options.
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ExoPlayer (HLS and DASH)
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Selected services we support
… and many more (just ask!)
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On-premise or hosted by us
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