## 1 Introduction
### Motivation  
Modern networks are increasingly characterized by a surge in video content, which dominates global data traffic. This proliferation of video is driven by the extensive growth of multimedia streaming, online gaming, digital advertising, and the pervasive influence of social networks [1]. Platforms such as Netflix, YouTube, and social media channels have transformed how audiences consume content, making video an indispensable medium for communication, entertainment, and marketing. In this context, high-frame-rate (HFR) video has drawn significant attention due to its ability to deliver smoother motion and enhanced visual clarity, both essential for immersive streaming experiences, realistic virtual environments, and dynamic advertisements [2]. For instance, recently, HFR videos have been gaining momentum in sports event broadcasting, providing enhanced speedating experiences for the viewers [3]. Moreover, modern consumer devices, ranging from smartphones with high-refresh-rate displays to large, high-definition televisions, are increasingly capable of rendering HFR content, further driving demand for superior video quality. The advent of next-generation mobile networks, such as 5G and beyond, has further accelerated this trend by providing higher data rates at lower latency. These improved network capabilities make it feasible to stream HFR content seamlessly, thereby meeting the rising consumer demand for high-quality, real-time video.  
However, despite these technological advancements, accurately assessing the Quality of Experience (QoE) for video streaming remains a formidable challenge. QoE encompasses the overall satisfaction of the user, reflecting not only objective transmission parameters but also the subjective perceptual quality of the content [4]. In the context of HFR content, this challenge is even more pronounced. HFR videos require substantially higher bandwidth and lower latency compared to standard videos, and even minor issues such as subtle motion artifacts, slight compression distortions, or occasional frame drops can disrupt the smooth, fluid motion that is essential for an immersive experience. These transmission impairments, which may go unnoticed in standard metrics, can significantly degrade user satisfaction by reducing the perceived realism and visual clarity [3]. Traditional objective quality metrics often fail to capture these intricate nuances of human perception, especially when HFR content's demanding nature magnifies the impact of even small errors in transmission [5].  
To address these challenges, robust and accurate models for video quality assessment (VQA) are imperative. Such models not only facilitate the optimization of streaming services to ensure a superior viewer experience but also enable network operators and content providers to monitor and dynamically adjust delivery parameters in real time. This work presents an optimized adaptive neuro-fuzzy inference system that leverages subtractive clustering for the accurate prediction of QoE in multimedia applications. The approach integrates key video parameters (video compression rate, frame rate, spatial information, and temporal information) to estimate the Mean Opinion Score (MOS) that closely reflects user perception. These parameters were chosen because compression rate directly influences visible artifacts, while video frame rate determines the smoothness of motion, spatial information captures the complexity and detail within scenes, and temporal information reflects the intensity of movement. Together, they encapsulate the technical and perceptual factors that impact overall video quality.