## 4 Review and Comparative Evaluation of VQA Models
### Review of VQA Models
Together, these metrics offer complementary insights into how subtle degradations, such as compression artifacts and motion irregularities, affect perceived video quality, especially in HFR content. The FRQM introduces a different approach to evaluating video quality with a focus on frame rate variations. As discussed by [PERSON] et al. in [20], it is designed to capture the impact of frame rate on the overall perceptual quality by jointly assessing spatial fidelity and temporal smoothness. Unlike metrics such as SpEED, which evaluate general spatio-temporal entropy differences, FRQM explicitly models the influence of frame rate variations, addressing the unique challenges posed by HFR video content. It quantifies how deviations from a target frame rate, along with associated artifacts, degrade the visual experience. The authors report that FRQM improved the prediction accuracy over all other tested VQA methods (available at the time) with relatively low complexity. DeepVQA represents a modern, deep-learning-based approach to video quality assessment that significantly departs from traditional entropy or signal-based metrics. DeepVQA leverages convolutional neural networks to automatically learn hierarchical features from video data, capturing both spatial details and temporal dynamics in a unified framework [21]. The network is trained end-to-end on large datasets of video sequences paired with subjective quality scores, enabling it to directly predict the MOS based on complex visual and motion characteristics. Another modern VQA model suitable for HFR video quality evaluation is GSTI. GSTI computes local gradients to capture the loss of spatial detail, as well as variations in motion dynamics that indicate temporal distortions [22]. By emphasizing the changes in gradient magnitudes, GSTI can differentiate between perceptually significant degradations and minor, non-intrusive variations. Moreover, the method is designed with computational efficiency in mind, facilitating its use in real-time applications. [PERSON] et al. in [23] propose the AVQBits model, i.e., its multiple variants, namely, AVQBits \(\ket{1}\)M3, AVQBits \(\ket{1}\)M1, AVQBits \(\ket{1}\)M0, AVQBits \(\ket{1}\)H0 \(\ket{1}\)s, and AVQBits \(\ket{1}\)H0 \(\ket{1}\)f, to more precisely predict video quality by accounting for bit rate variations and content complexity. The \"M\" versions primarily differ in their modeling of the bit rate impact on perceptual quality, with each variant (M3, M1, M0) representing different parameter configurations that balance model complexity and sensitivity. In contrast, the \"H0\" variants, distinguished by the suffixes \"s\" and \"r\", incorporate additional considerations: the \"s\" version emphasizes spatial characteristics, while the \"r\" variant focuses on finer-grained features. Another modern and cutting-edge VQA model is VMAF, developed by Netflix [24]. Unlike traditional metrics that focus on individual aspects such as PSNR or SSIM, VMAF fuses multiple quality indicators, combining pixel-level fidelity, structural similarity, and perceptual features, using machine-learning techniques. This fusion process results in a more robust assessment of both spatial and temporal artifacts, which is particularly valuable for high-resolution and HFR content [5]. [PERSON] et al. introduced BH-VQA, the first blind metric specifically designed for HFR VQA [2]. BH-VQA extracts multi-scale spatiotemporal features, such as motion coherence and texture complexity, and employs a trained regression model to predict MOS without requiring a reference video. When evaluated on the LIVE-YT-HFR dataset, it achieved high correlations with subjective ratings, demonstrating its suitability for real-time, no-reference quality monitoring in HFR streaming scenarios. Lastly, FLAME-VQA is a comprehensive VQA model originally developed in earlier work [6].