# Video Quality Modelling--Comparison of the Classical and Machine Learning Techniques
## 2 Related Work  
The quality of a video can be assessed using objective or subjective methods [4]. Due to the time-consuming and relatively lengthy nature of subjective methods, a lot of effort has been put into developing objective methods for assessing video quality. A good overview of this type of method can be found in Ref. [5], which presents a classification scheme of objective quality assessment methods. These methods can be divided differently depending on the adopted criteria. One of the possible classifications assumes a division into full-reference (FR), reduced-reference (RR), and no-reference (NR) methods. FR methods assume that, in addition to the evaluated material, reference video is available [6]. If only partial information about the source video is available, RR methods can be used [7]. The third case (NR method) is used when there is only access to a distorted signal, and then the quality of the video is assessed without knowing the source of the video material [8]. These metrics can be used naturally to evaluate a wide range of video degradation. However, apart from the above function of objective metrics, attempts are often made to use them to directly assess video quality. Most often, this is performed by mapping the values of individual objective metrics onto the subjective assessments of users obtained during parallel research with a group of testers. Often, objective quality indicators are used to build quality models and then automatically predict the average user rating, which is cheaper than obtaining this type of data from expensive subjective tests. The most frequently used objective metrics include: Aligned Peak Signal-to-Noise Ratio (APSNR) [9]; Delta, Mean Absolute Difference (MSAD) and Mean Squared Error (MSE) [10]; New Quality Index (NQI) [11; 12]; Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) [13]; Visual Information Fidelity (VIF) [14], Video Multi-Method Assessment Fusion (VMAF) [15], and Video Quality Metric (VQM) [16; 17]. There are many discussions and considerations in the scientific literature on the possibility of using specific objective metrics as predictors of the quality perceived by users [18]. Generally, these indicators can be divided into two groups. The first one includes purely mathematical video quality measures, such as Delta, MSAD, MSE, and PSNR, in which the error is calculated mathematically as the difference between the original and processed pixels. The second group includes metrics such as SSIM, NQI, and VQM, where the overall quality assessment also takes into account perceptual quality [19]. Depending on the method of operation, the values of individual metrics correlate with the results of subjective research to varying degrees [20; 21]. Despite significant progress in the development of objective techniques for measuring the quality of video transmission, the subjective assessment of video quality still plays an important role in video processing, especially when quality degrades at various stages of video signal acquisition, compression, transmission, and display. The final verifier of the quality of video transmission devices is their user, that is, a person. Taking into account the generally subjective evaluation of video quality, it can be said that the purpose of the assessment is to evaluate and differentiate the quality of the video signals that reach the viewer. The process of visual perception can be considered the viewer's reaction to the stimulus they receive. The viewer's reaction depends on the stimulus received as well as on the conditions in which the viewer finds themselves. It can therefore be said that the viewer's reaction depends not only on external factors affecting them at a given moment but also on internal factors. From a physiological and psychological point of view, the viewer's reaction can be considered in terms of a sensational and emotional reaction. A sensation reaction occurs as a result of a given stimulus exceeding certain sensitivity thresholds or thresholds of visual sensation categories.