# FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment
## 1 Introduction  
### Background and Motivation  
In the past decades, video streaming services have experienced an unprecedented surge in popularity, becoming an integral part of both fixed and mobile networks. The ease of access to high-quality content and the proliferation of internet-connected devices have contributed to this exponential growth, transforming video consumption into a ubiquitous phenomenon [1]. Nowadays, video traffic constitutes a substantial share of total internet traffic worldwide. In Ericsson's 2023 Mobility Report, video is identified as dominant content across all subscriber clusters, accounting for over 60% of total traffic in the sampled networks [2]. Moreover, the dynamic nature of video services has enabled them to evolve beyond mere entertainment platforms, emerging as a leading multimedia channel for product marketing across various industries, platforms, and user devices [3]. This rapid expansion of video streaming services has been accompanied by an increasing trend in the adoption of high video frame rates, aiming to enhance the overall viewing experience and immerse users in lifelike visual content. To this end, service providers strive to deliver content that aligns with users' perceived quality expectations [1]. Consequently, the demand for accurate quality assessment tools has become paramount, as the user Quality of Experience (QoE) plays a pivotal role in shaping user satisfaction, loyalty, and retention. Understanding and predicting user QoE in the context of video services is critical, as suboptimal viewing experiences can lead to user churn [4; 5]. In response to the escalating demand for precise QoE assessments, the research community has been engaged in the continuous pursuit of developing advanced models that can effectively anticipate user QoE in video streaming scenarios. The ITU-T P series ofrecommendations are at the forefront of this quest, often synthesizing the accumulated knowledge from this domain. Such video quality assessment (VQA) models not only serve as indispensable tools for optimizing video content delivery but also aid in efficient Quality of Service (QoS) management to meet user expectations [6]. Notwithstanding, predicting human perception of video quality is challenging due to the inherent complexity and subjectivity involved in the human visual and perceptual systems. Human perception is influenced by a myriad of factors, including individual preferences, cognitive biases, and contextual conditions, making it highly variable and difficult to model accurately. Additionally, different users may have diverse expectations and sensitivities, leading to variations in perceived quality [7; 8]. Even subtle changes in video content, such as frame-rate adjustments or compression rates, can impact user perception [9; 10]. Consequently, attaining a resilient and all-encompassing QoE assessment model necessitates a meticulous balance between objective technical metrics and subjective user evaluations, while considering the intricate nuances of human perception of video quality. The intricate interplay of these diverse influential factors compels researchers to formulate models that incorporate objective metrics alone, as well as those that synergistically blend objective metrics with subjective quality ratings. The objective metrics, used for VQA modeling, are usually extracted directly from video sequences or traffic flow properties. These metrics typically encompass parameters such as bit rate, frame rate, resolution, encoding quality (i.e., compression), delay, jitter, packet loss, etc. The models that rely only on objective metrics use well-defined algorithms to compute the output, providing an automated and quantifiable approach to assessing video quality. To develop a model, a large dataset of video sequences with associated technical parameters is required and is used to train the model, where statistical and machine learning techniques are commonly employed.