# Video Quality Modelling--Comparison of the Classical and Machine Learning Techniques
## 2 Related Work
More complex and difficult to analyse is the emotional reaction, which is not the result of the characteristics of the received signal but of the habits and individuality of the viewer. It can be said that the sensory reaction is a reflection of the visual image created in the human mind, while the emotional reaction is a reflection of the person's attitude towards this image. By ensuring appropriately stable evaluation conditions, it can be expected that the differences in impression reactions between individual viewers are smaller than the differences in their emotional reactions. Therefore, one of the main goals of objectifying video signal quality assessments is to limit the impact of emotional reactions on the final result as much as possible. This effect is achieved by introducing an appropriate number of assessment statistics, the appropriate selection and training of the assessment team, and the appropriate formulation of test tasks. Test tasks include the selection of test material, measurement principles, and the method for analysing the results obtained during measurements. A comprehensive review of subjective video quality assessment methodologies and objective algorithms for assessing video quality is presented in Ref. [22]. The process is even more complicated when assessing the quality of both audio and video content. An example of a work presenting the results of research in which audiovisual materials were examined, i.e., with simultaneous presentation of image and sound and various degrees of degradation, is Ref. [23]. The authors emphasise here that the audio has a significant impact on the overall quality perceived by users, but the video component has a decisive influence. Perceptual quality assessment is time-consuming, expensive, and requires subjective assessment with high skills with the participation of a specially trained group of viewers [24]. There are examples in the literature where, to reduce research costs, simpler tools and laboratory research methods are sought that provide results comparable to classical methods [25]. In recent years, machine learning models have become more common and are used not only to reduce the costs of quality modelling, among others, by introducing automation of activities but also to improve their accuracy. Ref. [26] presents an ML-based model for assessing video quality based on an analysis of the encoding video settings of transmitted contents and the intrinsic characteristics of the image to objectively estimate the mean opinion score (MOS) in correlation with subjective results. The use of data mining techniques to combine a collection of parameters associated with the video transmitted improves the performance of traditional quality evaluation. Some work has been devoted to comparing the performance of machine learning models to predict user-rated quality, depending on the type and number of inputs used to train the model [27]. Studies have also used artificial neural networks (ANNs) to build tools that will predict video quality based on user ratings. An example of such work is Ref. [28], where the authors present the concept of a measurement tool (VQMT) that allows an objective assessment of a given video material in close correlation with the perception of the human visual system (HVS). In summary, new approaches based on machine learning models, using larger amounts of data, provide the opportunity not only to automate the quality assessment process but also to develop models that are more accurate than existing, classical quality modelling methods.