# Video Quality Modelling--Comparison of the Classical and Machine Learning Techniques
## 4 Results
### ML-Based Results  
The authors proposed a linear regression model that shows the relationship between the subjective video quality predicted by the machine learning model, here called the machine learning score (MLS), and the video quality subjectively perceived by users (QoP) expressed on the five-point MOS scale. A plot of the fitted curves is shown in Figure 8. This chart compares video quality prediction results obtained from the machine learning model (MLS) with actual user-perceived quality (QoP) results.  
The experimental result presented here was obtained using the following steps: Initially, the ML model was trained based on a set of quality parameters expressed using all objective metrics analysed for the set of test video samples. In the next step, the trained model, based on a set of objective parameters describing another set of video materials, made a subjective quality prediction (MLS). In parallel, the same set of video materials was rated by users who assessed their quality on the MOS scale in subjective tests. In the last step, a comparison of the ratings obtained from the ML model and subjective research was made, and a linear regression model was proposed to describe the relationship between both sets of ratings. Finally, a quality model based on the machine learning score (MLS) was proposed:  
\[\text{QoP}=1.12*\text{MLS}-0.51, \tag{14}\]  
where QoP (quality of perception) is the predicted quality perceived by the users, and MLS denotes the value of the machine learning score obtained from the ML model. The parameters determined for the ML model are presented in Table 5. As can be seen, the ML-based linear regression model predicts user perception of video quality (QoP) significantly better than the models previously presented based on single classical metrics.  
Compared to Table 4, the residual sum of squares and the root mean square of the error are much smaller than the analogous values for each of the classic metrics. Finally, the coefficient of determination in this case is 5% higher than the highest coefficient of determination presented by the above-mentioned classic models. Based on the above research results, the proposed machine learning model can be used to predict the results of  
Figure 8: Fitted curves plot with confidence and predicted intervals for linear regression models based on different classical (CL) metrics.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Machine Learning Score (MLS)** \\ \hline Spearman’s r & 0.99 \\ R-Square (COD) & 0.97 \\ RSS & 1.82 \\ Root-MSE (SD) & 0.23 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The parameters of the ML-based linear regression model.  
subjective image quality assessments depending on the codec used, image resolution, and compression level (see Figure 9).  
Figure 9 shows a comparison of the video quality assessment results using two codecs and two spatial resolutions, carried out using two quality assessment methods. The first method involved a subjective assessment carried out by a group of users who rated them on a scale from 1 (worst quality) to 5 (best quality). In this case, the rating of each of the evaluated video samples was averaged (MOS). In the second case, the results of the prediction of perceived quality (MLS), based on a set of objective parameters carried out by a machine learning model, were presented.