# FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment
## 2 Related Works
They focus on two major impairments in streaming video: compression artifacts and rebuffering events. By combining multiple inputs such as video quality scores, rebuffering measurements, and memory-related data, the models aim to predict QoE on video streams impaired by both compression artifacts and rebuffering events. The experimental results showed that the proposed models approach human performance in predicting QoE. The research is continued in [29], where a large-scale crowdsourcing experiment is conducted to investigate how changes in screen size affect perceptions of video quality. The experiment involved rescaling video stimuli to different canvas sizes on participants' devices and collecting ratings on distorted videos. The study evaluated subjective modeling techniques and benchmarked objective quality models across screen sizes. The findings highlighted the importance of screen size in perceived video quality and the limitations of existing objective quality models in capturing the effect of screen size changes. The issue of video compression and its relation to user perception is also studied in [30]. Another example of using neural networks for the evaluation of the perceptual quality of streaming video can be found in [31]. Using a deep convolutional neural network (DCNN), the authors assessed the perceptual quality of streaming videos, incorporating the spatio-temporal characteristics of the videos in their evaluation. Another example of how learning a human visual behavior, in conjunction with spatial and temporal effects, usingneural network modeling can be found in [32], where the authors developed a Deep Video Quality Assessor (DeepVQA) that achieved high levels of assessment accuracy. [PERSON] et al. [33] propose a framework that uses Multi-Feature Fusion (MFF)-based Optimized Learning Models (OLMs) to predict video quality. The framework uses a combination of objective quality metrics and impairment factors to predict subjective quality scores. Machine learning algorithms were used in [34] for modeling QoE in user interactions with video streaming services, which make up a significant portion of mobile internet traffic. The research was aimed at modeling the overall quality of user experience with different types of network traffic. [PERSON] et al. [35] evaluate 13 existing QoE models for HTTP (Hyper Text Transfer Protocol) adaptive streaming. The models are evaluated using 12 different open databases with varying characteristics. The findings suggest that the performance of the models varies depending on factors such as video codec, session duration, and viewing devices. The Long Short-Term Memory (LSTM) model was identified as the best-performing model, but with still enough room for improvement, especially for different viewing devices and advanced video codecs. In [7], fuzzy logic was used to model user QoE for streaming videos. The model used three input parameters (packet loss rate, number, and the length of the packet loss intervals) to compute the Mean Opinion Score (MOS). Fuzzy logic can often be combined with other inference mechanisms to obtain the results. To this end, [PERSON] et al. [36] propose a fuzzy neural network to predict the opinion score distribution of image quality. A similar method could be applied to video quality assessment, but we must bear in mind the computational complexity of such solutions and their suitability for real-time QoS management. Recently, some researchers focused their attention on User-Generated Content (UGC), like [PERSON] et al. [37] or [PERSON] et al. [38]. In both works, the authors mainly concentrated on constructing the UGC databases suitable for future QoE research. Yet, both groups of researchers also proposed VQA models that can be used for QoE assessment [38] or for learning quality-aware audio and visual feature representations in the temporal domain [38].