## 4 Review and Comparative Evaluation of VQA Models
### Review of VQA Models  
This subsection begins by reviewing the most commonly used objective metric for video quality evaluation--PSNR. PSNR is a full reference metric that quantifies video quality by comparing the original video with a distorted version. It is computed as a logarithmic ratio of the maximum possible pixel value to the MSE between the two images or video frames [25]. While PSNR is straightforward and computationally efficient, its primary limitation lies in its inability to capture perceptual quality, particularly for HFR content. In HFR videos, the smoothness of motion and subtle temporal artifacts play a crucial role in user experience, yet PSNR only measures pixel-level differences without considering these temporal dynamics or taking into account a user's perceptual experience. As a result, PSNR may report high quality despite the presence of perceptible motion artifacts or compression distortions, making it less reliable for evaluating HFR video content in terms of human visual perception. The SSIM metric, introduced by [PERSON] et al. [15], represents an advancement over traditional PSNR metrics by evaluating video quality based on perceptual phenomena. SSIM assesses the similarity between the original and distorted images by comparing luminance, contrast, and structural information within local windows. This approach is designed to align more closely with human visual perception, as it accounts for structural distortions that are often more noticeable to the human eye than simple pixel-level errors. Building on the strengths and limitations of SSIM, MS-SSIM [16] further refines quality assessment by evaluating image quality across multiple scales. In this approach, images are decomposed into several resolutions, and the SSIM index is computed at each scale. These individual scores are then aggregated into a single overall quality measure that captures both coarse and fine perceptual details more effectively. MS-SSIM generally achieves a higher correlation with human subjective assessments compared to standard SSIM, as it is more sensitive to distortions that affect image details at different resolutions. Next in line in the evolution of similarity indexes is the FSIM. It leverages phase congruency, which serves as a robust measure of feature significance, alongside gradient magnitude to quantify the similarity between the original and distorted images [17]. Unlike SSIM, which primarily compares luminance, contrast, and structural information, FSIM emphasizes the preservation of visual features, thus providing a more perceptually relevant quality assessment. This metric has demonstrated superior performance in capturing subtle details and distortions, particularly in scenarios where fine structural information is paramount. However, both SSIM and MS-SSIM, as well as FSIM, exhibit limitations when applied to HFR video content. While SSIM generally provides a better correlation with subjective quality ratings than PSNR, it does not capture the temporal dynamics of HFR sequences where, as discussed earlier, temporal smoothness and subtle motion artifacts are critical to user experience. Similarly, despite their improved spatial evaluation, MS-SSIM and FSIM still encounter the same challenges when applied to HFR video content. Contrary to models reviewed thus far, which focus on pixel differences, structural similarity, or feature preservation, both ST-RRED [18] and SpEED [19] utilize entropy-based approaches to assess video quality by measuring the differences in information content between the reference and distorted signals. ST-RRED focuses on computing entropy differences over local spatiotemporal regions using a reduced-reference strategy, which highlights changes in texture and motion consistency. In contrast, SpEED extends this concept by evaluating entropy differences across multiple scales and temporal windows, thereby providing a more comprehensive view of both spatial and temporal distortions while emphasizing computational efficiency.