# Video Quality Modelling--Comparison of the Classical and Machine Learning Techniques
## 4 Results
### Objective Quality Assessment  
However, since the relationship between the encoding speed and the quality perceived by users may vary depending on the analysed video [37; 38], content providers and streaming platforms should take this into account when preparing content, which should be tailored to the network bandwidth requirements and the type of user equipment while maintaining the highest possible quality. This makes the assessment of video quality an activity that must be repeated over and over again for different types of content. Since subjective assessment in such a case becomes a very demanding and expensive activity, it is necessary to use objective methods. The results obtained from the objective quality evaluation of the videos examined are presented in Figure 5. Each of the metrics shows that as the encoding speed increases, the quality of the presented video increases. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Bitrate**} & \multicolumn{6}{c}{**H.264**} & \multicolumn{6}{c}{**H.265**} \\ \cline{2-13}  & \multicolumn{3}{c}{**1280 \(\times\) 720**} & \multicolumn{6}{c}{**1920 \(\times\) 1080**} & \multicolumn{6}{c}{**1280 \(\times\) 720**} & \multicolumn{6}{c}{**1920 \(\times\) 1080**} \\ \cline{2-13}
**[kbps]** & **MOS** & **S** & **\(\delta\)** & **MOS** & **S** & **\(\delta\)** & **MOS** & **S** & **MOS** & **S** & **\(\delta\)** \\ \hline
300 & 1.16 & 0.43 & 0.13 & 1.05 & 0.32 & 0.1 & 1.09 & 0.29 & 0.12 & 1.13 & 0.34 & 0.14 \\
400 & 1.33 & 0.57 & 0.17 & 1.19 & 0.45 & 0.13 & 1.61 & 0.5 & 0.2 & 1.65 & 0.49 & 0.2 \\
500 & 1.81 & 0.55 & 0.16 & 1.35 & 0.57 & 0.17 & 2.09 & 0.42 & 0.17 & 2.04 & 0.64 & 0.26 \\
600 & 2.24 & 0.66 & 0.2 & 1.58 & 0.59 & 0.18 & 2.48 & 0.59 & 0.24 & 2.48 & 0.51 & 0.21 \\
700 & 2.71 & 0.56 & 0.17 & 1.98 & 0.64 & 0.19 & 2.87 & 0.34 & 0.14 & 2.83 & 0.58 & 0.24 \\
800 & 2.86 & 0.64 & 0.19 & 2.4 & 0.54 & 0.16 & 3.09 & 0.42 & 0.17 & 3.13 & 0.76 & 0.31 \\
900 & 3.12 & 0.54 & 0.16 & 2.81 & 0.55 & 0.16 & 3.26 & 0.45 & 0.18 & 3.35 & 0.49 & 0.2 \\
1000 & 3.26 & 0.54 & 0.16 & 3.16 & 0.57 & 0.17 & 3.43 & 0.51 & 0.21 & 3.48 & 0.59 & 0.24 \\
1500 & 3.74 & 0.66 & 0.2 & 3.86 & 0.47 & 0.14 & 3.78 & 0.42 & 0.17 & 3.91 & 0.51 & 0.21 \\
2000 & 4.07 & 0.7 & 0.21 & 4.26 & 0.49 & 0.15 & 4.09 & 0.67 & 0.27 & 4.22 & 0.52 & 0.21 \\
2500 & 4.19 & 0.59 & 0.18 & 4.4 & 0.54 & 0.16 & 4.3 Figure 5: The relationship between coding bitrate and video quality, expressed in terms of objective metrics, using H.264 and H.265 codecs and 720p and 1080p resolutions. The examined metrics are: (**a**) APSNR, (**b**) Delta, (**c**) MSAD, (**d**) MSE, (**e**) NQI, (**f**) PSNR, (**g**) SSIM, (**h**) VIE, (**i**) VMAF, and (**j**) VQM. It should be emphasised here that the interpretation of the results expressed by individual metrics differs. Each of these metrics can be used to objectively assess the degree of deterioration of the tested video compared to the reference material, which is often uncompressed video. However, a high rating expressed by a given metric does not always correspond to the high quality of the assessed video material. Sometimes such a high value means a low degree of degradation of the test video compared to the reference material. Some of the presented measures show a direct proportional correlation with the degree of compression of the assessed material in relation to the reference material, while others show an inverse relationship. An important task is to find an appropriate criterion for comparing individual metrics. This criterion may be the correlation coefficient between the values of a given metric and the subjective evaluation of the quality expressed by users [39; 40]. A high correlation between the values of objective measures and the results of subjective user assessments would justify the sense of building a quality model. Such a model, taking into account the values of individual metrics, would allow for predicting the quality perceived by users without the need to conduct costly and long-term subjective research.