Video quality
Video quality can be evaluated objectively (by mathematical models) or subjectively (by asking users for their rating).
Such systems encode video streams and transmit them over various kinds of networks or channels.
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.
[1] 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.
The terms model and metric are often used interchangeably in the field to mean a descriptive statistic that provides an indicator of quality.
Unlike a panel of human observers, an objective model should always deterministically output the same quality score for a given set of input parameters.
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.”[4] Objective models can be classified by the amount of information available about the original signal, the received signal, or whether there is a signal present at all:[5] Some models that are used for video quality assessment (such as PSNR or SSIM) are simply image quality models, whose output is calculated for every frame of a video sequence.
While this method is easy to implement, it does not factor in certain kinds of degradations that develop over time, such as the moving artifacts caused by packet loss and its concealment.
Examples can be seen in the table below In theory, a model can be trained on a set of data in such a way that it produces perfectly matching scores on that dataset.
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.
