# FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment
## 5 Results and Discussion
### Evaluation of the Model Output  
The evaluation of the performance of our proposed VQA model is carried out by comparing the obtained outputs with multiple data subsets, including the training, test, and complete dataset. We used common metrics for the evaluation of the output accuracy, namely, coefficient of determination (R2), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Spearman Rank-Order Correlation Coefficient (SROCC), and Pearson Correlation Coefficient (PCC). Figure 6 depicts the comparison between different MOS ratings, for every video in the LIVE-YT-HFR database (480), obtained from the datasets and our model, while the performance metrics can be found in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Training Data** & **Test Data** & **Complete Dataset** \\ \hline R2 & 0.827 & 0.7447 & 0.8253 \\ MSE & 8.7819 & 13.6213 & 8.7605 \\ RMSE & 2.9634 & 3.6907 & 2.9598 \\ SROCC & 0.8977 & 0.8455 & 0.8961 \\ PCC & 0.9096 & 0.8632 & 0.9086 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The evaluation of the predictive performance of the model. Figure 6: Comparison of the MOS values between the model output and: (**a**) training dataset; (**b**) test dataset; (**c**) complete dataset. The performance of the model on the training dataset shows a high level of correlation and accuracy. The R\({}^{2}\) value indicates that about 82.7% of the variance in the output of the model can be explained by the variance in the training dataset MOS. The low MSE and RMSE values of 8.7819 and 2.9634, respectively, confirm the ability of the model to minimize assessment errors. In addition, the SROCC and PCC values of 0.8977 and 0.9096, respectively, demonstrate the strong positive monotonic and linear correlation between the assessments of the model and the MOS values from the training dataset. When evaluating the performance of the model on the independent test dataset, we find a slightly decreased but still significant level of predictive ability. The SROCC and PCC values of 0.8455 and 0.8632, respectively, illustrate a robust correlation between the assessments of the model and the test dataset MOS. The performance of our model over the entire dataset further underscores its effectiveness. Comparison of performance across the three datasets provides valuable insights into the generalization capabilities of the model. The high R\({}^{2}\) and correlation coefficients in all datasets indicate that the model has learned meaningful patterns from the training data and can make accurate predictions for unseen samples. The slightly higher assessment errors in the test dataset compared to the training dataset can be attributed to the inherent variability in the data, i.e., subjective quality perception ratings and fewer ratings per video in the test dataset. Nonetheless, the consistent and robust correlation observed in both SROCC and PCC metrics underscores the reliability of the model, especially when keeping in mind the different genres of the sequences (Figure 1). The results also show how the utilization of fuzzy logic holds promise in addressing the inherent complexities of video quality assessment, as it acknowledges the multifaceted nature of visual perception. The model's ability to encompass both objective technical attributes and subjective perceptual aspects within a unified framework offers a novel approach to VQA. Moreover, the incorporation of fuzzy logic introduces a level of interpretability and adaptability, allowing the model to effectively capture the intricate interdependencies and uncertainties that characterize human judgment. Since the model is trained with HFR video content and built for its quality assessment, it makes sense to compare how different pairs of input parameters affect its output. To this end, Figure 7a shows the model behavior for a range of Video CRF and Video FPS values. Figure 7b does the same for another input pair, particularly of interest in HFR videos, namely, Video FPS and Video TI.