# FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment
## 2 Related Works  
As discussed in the previous section, video content has become the dominant form of media on modern networks. The demand for content is increasing rapidly, leading to the expansion of Internet traffic. Large-scale video streaming services like YouTube, Netflix, and Hulu play a significant role in contributing to this growth [15]. This sparked the development of numerous quality assessment models in parallel. These models have been designed to evaluate the quality of video-specific formats and properties, as well as for the specific network conditions and contexts in which the videos are delivered to end users. Some of these models are already well-established and frequently used, such as Peak-Signal-to-Noise-Ratio (PSNR), Structural Similarity (SSIM) index, Multiscale Structural SSIM (MS-SSIM) [16; 17] respectively, Feature Similarity Index (FSIM) [18], the Spatio-Temporal Reduced Reference Entropic Differencing (ST-RRED) algorithm developedin [19], and the Spatial Efficient Entropic Differencing for Quality Assessment (SpEED-QA) model from [20]. It is important to note that delving into that extensive array of studies and well-established algorithms exceeds the confines of this review. Thus, our focus will be solely on recent developments in the field of predicting 2D video quality by incorporating subjective ratings into the inference systems of the models, since our research contributes specifically to that domain. In [21], the authors propose a method to predict the subjective video quality based on the objective video quality measure (specifically, PSNR) using a sigmoid function model, allowing for the generation of a larger dataset without the need for costly and time-consuming subjective tests. [PERSON] et al. present a method called Direct Scaling (DSCAL) for assessing the perceptual quality dimensions of video degradations, also aiming to reduce the experimental effort and allowing for more test conditions to be evaluated [22]. The linear quality prediction model based on the identified perceptual dimensions showed a strong correlation with subjective test results, indicating its potential for accurately predicting overall video quality. [PERSON] et al. [23] introduce a video quality model that takes into account the perceptual impact of variable frame delays in videos. The model uses perceptual features extracted from spatial-temporal blocks and a long edge-detection filter to predict video quality by measuring multiple frame delays. Factor analysis was used in [24] to analyze the QoS variables, bit stream, and basic video quality metrics to estimate and predict the subjective quality. To showcase the vastness of subjective factors impacting the perception of video quality, we review [25], which delves into the assessment of user engagement within adaptive bitrate video streaming. The authors investigated the relationship between viewing time and video quality using two subjective evaluations. The findings revealed that, in the case of low-quality videos, the decline in viewership follows a logarithmic pattern over time. Interestingly, instances of video stalling prompted users to discontinue playback after a 5-s wait. The rate at which users ceased playback post-stalling was contingent on factors such as the stalling location, duration, and quality influenced by coding aspects. To further quantify these observations, the authors formulated a baseline model solely incorporating stalling attributes. Additionally, they proposed a predictive model aimed at estimating video completion rates. The effect of stalling the video playback is also studied in [26]. Adding to the number of factors influencing user QoE, [PERSON] found that packet loss in Internet Protocol Television (IPTV) transmission significantly affects the perceived quality, with burst loss having a greater impact than random loss [27]. [PERSON] et al. [28] propose a variety of recurrent dynamic neural networks that can predict QoE using subjective QoE databases.