# Video Quality Modelling--Comparison of the Classical and Machine Learning Techniques
## 5 Discussion  
The analysis of the results obtained in the above-described research shows that the proposed machine learning model allows one to predict the results of the subjective assessment of video material based on its measurable objective features in a better way than the existing classic quality models. Classic models describing the relationship between the quality perceived by users and the quality of measurable features of the analysed video materials are usually based on a very limited number of measurable parameters of the evaluated video. One of the reasons for these limitations is the desire to limit the complexity of the final model, which should be simple enough to be suitable for implementation in practice. Otherwise, a model that is too extensive may, on the one hand, lose its generality of application and, on the other hand, not be easy to use. Most of the classic quality models presented in this work, based on single objective metrics, are characterised by a high correlation with the results of subjective research. As shown in Table 3, only the model based on the Delta metric showed a low correlation coefficient, while the remaining metrics  
Figure 9: Video quality as a function of coding bitrate—a comparison of subjective assessment results (MOS) and the scores predicted by the ML model (MLS) for two codecs and two spatial resolutions: (**a**) H.264, 720p; (**b**) H.264, 1080p; (**c**) H.265, 720p; and (**d**) H.265, 1080p. correlated with subjective results at a level not lower than 0.96. The highest correlation coefficient (r = 0.99) was obtained for the VQM metric. It should be noted that the results obtained here do not differ significantly from the values presented in the literature and, in many cases, even exceed them. For example, in Ref. [41], the highest values of the correlation coefficient for identical metrics (different databases were tested) were r\({}_{\text{PSNR}}\) = 0.89, r\({}_{\text{PSIM}}\) = 0.92, and r\({}_{\text{VIF}}\) = 0.98, respectively. While the correlation coefficient presented in Ref. [41] for the VIF metric was slightly better than in our case (r\({}_{\text{VIF}}\) = 0.97), the other two metrics of our study gave much better results (r\({}_{\text{PSNR}}\) = 0.97, r\({}_{\text{SSIM}}\) = 0.98). In Ref. [5], the authors also compared various objective video quality metrics, such as PSNR, SSIM, and VQM, and checked how they correlate with subjective user ratings. According to the authors of the work, objective quality measures show a high correspondence to subjective quality grades, i.e., close to or higher than 0.9. The values presented there are lower than those obtained in our research. The authors of Ref. [4] give a comprehensive survey of the evolution of video quality assessment methods, analysing their characteristics, advantages, and drawbacks. They discuss, among others, relationships between quality of service (QoS) and quality of experience (QoE) for different kinds of models, i.e., engineering approach models (based on modelling video artefacts such as edginess, etc.) and structural similarity-based models. The correlation of the results of the quality assessment carried out using these models with the quality perceived by users during subjective tests ranges from 0.77 to 0.97. In Ref. [42], the authors explore the relationship between image information and visual quality. They proposed a measure of visual information fidelity for the assessment of image quality and validated the performance of the algorithm with an extensive subjective study involving hundreds of images. Depending on the objective metric used, the authors obtained correlation coefficients (SROCC) ranging from 0.82 to 0.95. In our research, we would like to draw attention to an additional parameter, which is the coefficient of determination (COD), showing how well the model (here a linear regression model based on a specific objective metric) predicts the result (here the video quality perceived by users).