## 5 Conclusions  
This study was motivated by the increasing demand for effective video quality assessment methods amid the rapid growth of HFR video streaming. The proposed adaptive neuro-fuzzy inference system, developed by integrating subtractive clustering with ANFIS, demonstrated superior performance on the LIVE-YT-HFR dataset, achieving lower RMSE and higher correlation coefficients compared to traditional metrics. This research contributes to the field by showing that a compact, data-driven fuzzy logic model with a streamlined rule base can accurately capture the complex interplay among video compression rate, frame rate, spatial information, and temporal information. Moreover, the model's efficiency and robustness offer significant advantages for perceptual quality prediction in HFR video content. Future work will proceed in two complementary directions. First, ANFIS-VQA will be retrained and benchmarked on additional public databases covering a wider spectrum of resolutions, codecs (H.264/H.265), and realistic network degradations, so that the model's generalisability across heterogeneous streaming environments can be rigorously validated. Second, the present fuzzy framework will be combined with advanced machine-learning techniques to refine temporal analysis and further improve predictive accuracy.  
Conceptualization, M.M.; methodology, M.M. and S.M.; software, M.M. and M.P.; validation, M.M. and S.M.; formal analysis, S.M. and M.M.; investigation, S.M. and M.M.; resources, M.M. and I.G.; data curation, M.M. and S.M.; writing--original draft preparation, S.M. and M.M; writing--review and editing, M.M., S.M., M.P. and I.G.; visualization, M.M. and S.M.; supervision, S.M. and M.M. Both [PERSON] and [PERSON] acknowledge that the lead authors on this manuscript are M.M. and S.M. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**VQA Model** & **RMSE** & **PCC** & **SROCC** \\ \hline PSNR & 9.023 & 0.6685 & 0.695 \\ SSIM & 10.819 & 0.4526 & 0.4494 \\ MS-SSIM & 10.726 & 0.4673 & 0.4898 \\ FSIM & 10.502 & 0.5008 & 0.5251 \\ ST-RRED & 10.431 & 0.5107 & 0.5531 \\ SpEED & 10.866 & 0.4449 & 0.4861 \\ FRQM & 10.804 & 0.452 & 0.4216 \\ DeepVQA & 11.441 & 0.3329 & 0.3463 \\ GSTI & 7.422 & 0.791 & 0.7909 \\ AVQBits I M3 & * & 0.7805 & 0.7118 \\ AVQBits I M1 & * & 0.5528 & 0.4809 \\ AVQBits I M0 & * & 0.5538 & 0.4947 \\ AVQBits I H0 I s & * & 0.7887 & 0.7324 \\ AVQBits I H0 I f & * & 0.7242 & 0.674 \\ VMAF & 8.587 & 0.7071 & 0.7303 \\ FLAME-VQA & 2.9598 & 0.9086 & 0.8961 \\ BH-VQA (LSVQ) & 6.303 & 0.847 & 0.816 \\ BH-VQA (ImgNet) & 7.401 & 0.795 & 0.784 \\ New ANFIS-VQA & 2.9091 & 0.9174 & 0.9048 \\ \hline \hline \end{tabular}  
* Not reported in the original research [23].  
\end{table}
Table 4: Comparative table of the achieved performance metrics.  
**Funding:** This research is funded by the University of Zagreb through the grants for core financing of scientific and artistic activities of the University of Zagreb in the academic year 2023/2024, under the project (210275) \"Modeling Framework for the Development and Application of Assistive Technologies\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The analyzed video database and the related subjective ratings used of the videos for the modeling can be found on this link: [[https://live.ece.utexas.edu/research/LIVE_YT_HFR/LIVE_YT_HFR/index.html](https://live.ece.utexas.edu/research/LIVE_YT_HFR/LIVE_YT_HFR/index.html)]([https://live.ece.utexas.edu/research/LIVE_YT_HFR/LIVE_YT_HFR/index.html](https://live.ece.utexas.edu/research/LIVE_YT_HFR/LIVE_YT_HFR/index.html)) (accessed on 3 February 2025).  
**Conflicts of Interest:** The authors declare no conflicts of interest.