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Perceived video degradation
Video quality is a characteristic of a video passed through a video transmission or processing system that describes perceived video degradation (typically compared to the original video). Video processing systems may introduce some amount of distortion or artifacts in the video signal that negatively impact the user's perception of the system. For many stakeholders in video production and distribution, ensuring video quality is an important task. Video quality evaluation is performed to describe the quality of a set of video sequences under study. Video quality can be evaluated objectively (by mathematical models) or subjectively (by asking users for their rating). Also, the quality of a system can be determined offline (i. e., in a laboratory setting for developing new codecs or services) or in service (to monitor and ensure a certain level of quality).

Since the world's first video sequence was recorded and transmitted, many video processing systems have been designed. Such systems encode video streams and transmit them over various kinds of networks or channels. In the age of analog video systems, it was possible to evaluate the quality aspects of a video processing system by calculating the system's frequency response using test signals (for example, a collection of color bars and circles). Digital video systems have almost fully replaced analog ones, and quality evaluation methods have changed. The performance of a digital video processing and transmission system can vary significantly and depends on many factors, including the characteristics of the input video signal (e. g., amount of motion or spatial details), the settings used for encoding and transmission, and the channel fidelity or network performance.

Objective video quality models are mathematical models that approximate results from subjective quality assessment, in which human observers are asked to rate the quality of a video. In this context, the term model may refer to a simple statistical model in which several independent variables (e. g., the packet loss rate on a network and the video coding parameters) are fit against results obtained in a subjective quality evaluation test using regression techniques. A model may also be a more complicated algorithm implemented in software or hardware.

The terms model and metric are often used interchangeably in the field to mean a descriptive statistic that provides an indicator of quality. The term “objective” refers to the fact that, in general, quality models are based on criteria that can be measured objectively, that is, free from human interpretation. They can be automatically evaluated by a computer program. Unlike a panel of human observers, an objective model should always deterministically output the same quality score for a given set of input parameters. Objective quality models are sometimes also referred to as instrumental (quality) models, in order to emphasize their application as measurement instruments. Some authors suggest that the term “objective” is misleading, as it “implies that instrumental measurements bear objectivity, which they only do in cases where they can be generalized.”

Objective video quality models can be used in various application areas. In video codec development, the performance of a codec is often evaluated in terms of PSNR or SSIM. For service providers, objective models can be used for monitoring a system. For example, an IPTV provider may choose to monitor their service quality by means of objective models, rather than asking users for their opinion, or waiting for customer complaints about bad video quality. Few of these standards have found commercial applications, including PEVQ and VQuad HD. SSIM is also part of a commercially available video quality toolset (SSIMWAVE). VMAF is used by Netflix to tune their encoding and streaming algorithms, and to quality control all streamed content. It is also being used by other technology companies like Bitmovin and has been integrated into software such as FFmpeg. An objective model should only be used in the context that it was developed for. For example, a model that was developed using a particular video codec is not guaranteed to be accurate for another video codec. Similarly, a model trained on tests performed on a large TV screen should not be used for evaluating the quality of a video watched on a mobile phone.

When estimating quality of a video codec, all the mentioned objective methods may require repeating post encoding tests in order to determine the encoding parameters that satisfy a required level of visual quality, making them time consuming, complex and impractical for implementation in real commercial applications. There is ongoing research into developing novel objective evaluation methods which enable prediction of the perceived quality level of the encoded video before the actual encoding is performed.

The main goal of many objective video quality metrics is to automatically estimate the average user's (viewer's) opinion on the quality of a video processed by a system. Procedures for subjective video quality measurements are described in ITU R recommendation BT.500 and ITU T recommendation P.910. In such tests, video sequences are shown to a group of viewers. The viewers' opinion is recorded and averaged into the mean opinion score to evaluate the quality of each video sequence. However, the testing procedure may vary depending on what kind of system is tested.