# FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment
## 6 Conclusions  
By harnessing the resources of an open-access LIVE-YT-HFR video database and utilizing results obtained from a subjective experiment involving 85 participants who assessed the quality of 480 test sequences, we have successfully engineered a robust fuzzy logic-based model and named it FLAME-VQA. This model exhibits the capacity to evaluate video quality of low and high frame rates, as perceived by users. The framework operates based on four key input parameters intrinsic to the video: video frame rate, CRF, SI, and TI properties.  
What distinguishes our model is its ability to perform evaluations without necessitating further human experimentation or imposing significant computational overhead. This aspect positions FLAME-VQA as a compelling alternative to approaches rooted in machine learning or neural networks. Notably, we have demonstrated that fuzzy logic shines particularly in scenarios marked by uncertainty and ambiguity, a characteristic inherent to subjective experimentation. Through the integration of fuzzy sets and the derivation of membership functions, we have effectively modeled intricate phenomena, showcasing the model's high accuracy in conducting video quality assessments across a diverse array of streaming scenarios (the SROCC and PCC values higher than 0.89). Incorporating fuzzy logic as the underlying engine of our model not only validates its efficacy in reflecting human perception but also demonstrates a forward-looking approach to bridging the gap between technical metrics and subjective experience.  
Looking ahead, our research trajectory will be devoted to the continual refinement and enhancement of this model, as well as testing the model performance on different datasets, i.e., video databases. Presently, the scope of the model is confined to four input parameters, and its applicability is limited to videos featuring a maximum of 120 frames per second. A promising avenue for future development involves the incorporation of video resolution as an additional input parameter within the model. This expansion promises to further enrich the FLAME-VQA capacity to accurately gauge user QoE across a broader spectrum of video streaming contexts.  
Conceptualization, S.M. and M.M.; methodology, S.M. and M.M.; software, M.M.; validation, S.M.; formal analysis, S.M. and M.M.; investigation, S.M. and M.M.; resources, M.M.; data curation, S.M.; writing--original draft preparation, S.M. and M.M.; writing--review and editing, S.M. and M.M.; visualization, S.M. and M.M.; supervision, S.M. and M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
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 5 June 2023).  
The authors declare no conflict of interest.