# FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment
## 1 Introduction
### Background and Motivation
The main advantage of these types of models, relying solely on objective parameters, is their efficiency and ease of implementation, as they do not require human involvement in the assessment process. However, they may fall short in capturing the subjective and perceptual aspects of user experience, thus failing to meet the essence of the QoE concept [7]. On the other end of the spectrum, we have the models that incorporate subjective user ratings into their inference systems. These models take a different approach: human subjects are involved in rating the quality of video sequences based on their personal experiences and subjective judgments. These ratings are typically obtained through controlled experiments, where participants watch a set of video sequences and provide quality ratings, often using rating scales or scoring systems. The rating scales usually employ linguistic variables (e.g., bad, poor, fair, good, and excellent) to capture the user perception of the quality. To develop such models, a subjective testing dataset comprising video sequences and corresponding subjective ratings is collected. The model is trained to identify patterns and relationships between the video attributes and the ratings. The resulting model, unlike its objective counterpart, directly addresses the human perceptual aspects of QoE. It can describe variations in user experiences that may not be reflected in objective metrics alone, making it more suitable for accurately assessing user QoE in complex and dynamic video streaming scenarios [11]. However, developing such a model requires significant effort in conducting subjective tests, involving a large number of participants and careful experimental design to obtain reliable and representative data. In light of these challenges and opportunities, this research aims to address the pressing need for an accurate quality assessment model tailored explicitly for immersive high-frame-rate video streaming services. By leveraging a video database consisting of 480 video sequences, enriched with 19,000 quality ratings from 85 test subjects (LIVE-YT-HFR database available in [12] and presented in [9; 13]), we propose a novel fuzzy logic-based approach that endeavors to model user perception of quality more effectively. Employing fuzzy logic for this task yields several advantages over alternative methods like machine learning and neural networks. One key strength of fuzzy logic lies in its adeptness at handling uncertainty and vagueness, both of which are inherent attributes of human perception [14]. Unlike traditional binary logic or crisp systems that enforce rigid true-or-false classifications, fuzzy logic allows for gradual transitions and nuanced decision-making, mirroring the way humans process information. This flexibility enables fuzzy logic to capture the intricacies of user QoE more effectively [7]. Additionally, it provides an intuitive and interpretable framework, allowing researchers to easily comprehend and analyze the relationships between various input variables, such as video characteristics, network conditions, user preferences, and social context, and their corresponding output assessments, specifically the video quality ratings in our case. Fuzzy logic's innate ability to handle uncertainty and imprecision aligns with the subjective nature of human perception, making it an alluring choice for capturing the intricacies and subtleties that underlie user QoE.