Humans are audiovisual animals. We dedicate two out of our five senses to communicate in that way. We do prefer watching to listening, and talking to typing/writing. That gives importance and relevance to the human quality of experience of communicating by using video contents.
Don ́t confuse quality of experience of video contents with quality of the video signal: Video contents quality is about the experience of using those two senses and the quality of experience of communicating among us by using certain video contents. Video contents are the soul and feeling of a videoconference, a TV advertisement, watching a movie/serie or a life sport, etc. Quality of experience of video contents is not about the nature of them, you can dis/like what you are watching, videoconferencing, etc., but you ́ll always have an opinion on those video contents quality as a viewer. Are they from an audiovisual point of view satisfactory or not to you as end user/viewer?. And, how do you rate them?. Viewers care of quality of experience of video contents, and score them somehow subjectively. This is QoE.
Quality of the video signal is different; it is about mainly these three steps: the production, the transmission (de/coding), and the broadcasting of video signal components, and its caring during those three steps progress. Good quality of the video signal not always assures satisfactory quality of experience of video contents. Media engineers care of Quality of the video signal and measure it objectively. This is Quality of Signal.
Quality is the degree of excellence of something. Within the networked media ecosystem, there are many approaches to provide a term for “quality”. However, in most of the digital multimedia entertainment applications, the main and real interest is focused on the overall QoE (Quality of Experience) perceived by the final end user / viewer. QoE represents how good a video looks, how good an audio sounds, how good a combined audiovisual content is perceived or how well interactivity works with a specific audiovisual service.
Quality of Service is when referring to video contents, equivalent to any other service. It ́s about: the coverage, the service functionality continuity, the reliability provided, the device compatibility, and the assurance of the quality of signals and functions involved in letting availability.
Let ́s use the mobile service as an example. Good quality of service requires coverage (there is radio signal coverage), continuity (it ́s always on, available 24×7), it ́s reliable (when using your mobile in e.g. a call, it does not get discontinued randomly), it works in all commercial mobile vendor terminals (regardless the manufacturer brand), and requires mobile network assures 99,999% of time minimum satisfactory network service is accomplished.
OBJECTIVE / ENGINEERS
Audiovisual services are intended for people. Nowadays, audiovisual platforms and OTT (Over the Top) services which offer a large catalog of series, movies and documentaries, aim greater expectation on end user/viewer regarding the quality of these contents and it implies the success or non-success of a certain service.
How to measure the quality of an audiovisual content is not easy, mainly because it is conditioned not only by an objective measurement but also a subjective assessment, and this last one is very difficult to determine. In any audiovisual chain, video and audio are degraded during the acquisition, compression, transmission, processing and visualization. The distortions directly affect the final quality perceived by the end users. Typical degradations, in the case of the video, are contrast or colour issues due to the nature of the scene, blurring, blocking, loss of bitrate caused by the coding, loss of packets and latency in the transmission of the content, among others.
Measuring the perceived quality of an audiovisual content by end users has become a very important objective for broadcasters and content providers. The audiovisual content traffic through the Internet along with the new TV channels have grown significantly. It is essential for them to have knowledge about what they are providing to the final users and how well users perceive it.
When the contents are transformed into other formats and transmitted, either through the Internet or conventional TV (cable, terrestrial or satellite diffusion), more or less transmission errors will happen in the audio and/or in the video. Having mechanisms that detect these errors are necessary to determine the quality of the signal.
The success of an audiovisual content is related to the number of visualizations and the audience, and the quality of the content plays an important role in this. However, estimating the perceived quality is not easy. Mainly because determining how well a video looks, how well an audio sounds, and how good is the interaction with the service or application is subjective and depends on the human perception.
The subjective metric used to assess the final perceived quality is known as QoE (Quality of Experience). Using the ITU’s (International Telecommunications Union) definition, the QoE is “the overall acceptability of an application or service, as perceived subjectively by the end user”. This metric takes into account the type of content, the degradations of the signal, the expectations, experiences and user perceptions (related to the Human Visual System and Human Auditory System), network conditions and device capabilities. QoE would be the degree of satisfaction or disapproval of an end user/viewer with a specific service, taking into account all the previously mentioned factors.
All these factors described above have to be considered when estimating the QoE. The estimation can be done with subjective assessments where users evaluate the quality of a set of degraded sequences following a specific methodology or with objective assessments, using mathematical algorithms to measure the audiovisual signal degradation and network conditions, taking into account the characteristics of the audiovisual content and the models of the human perception.
That ́s what Video-MOS is all about, launching a tool service by getting the Machine Learning to help MOS becoming the right subjective assessments and modelling the human perception in conjunction.