## 4 Review and Comparative Evaluation of VQA Models
### Performance Comparison  
In this section, the performances of the VQA models, reviewed earlier, are evaluated using the LIVE-YT-HFR dataset. For the sake of clarity, the current model, developed within this study, was named a new ANFIS-VQA model. The discussion starts with Figure 7a, which illustrates how the new ANFIS-VQA model's predictions align with the ground truth MOS ratings from the complete LIVE-YT-HFR dataset. The points are well-correlated along an upward-sloping linear trend, indicating that the model consistently approximates viewer perception. This tight clustering around the best-fit line confirms that the model effectively captures the nonlinear relationships among the input variables used for the modeling, yielding accurate MOS predictions across a diverse range of video content. To investigate the limitations of the new ANFIS-VQA model, the five video sequences with the largest absolute prediction errors were identified (Figure 7b). The worst-case sequences reveal two primary failure modes: sequences with very high compression (e.g., sequence ID 114, CRF = 58, FPS = 120, SI = 22.9, TI = 20.4, error = 8.68) and sequences with low spatial detail or moderate motion (e.g., sequences 366 and 363, CRF = 0 but SI \(\approx\) 44, TI \(\approx\) 44-60, error \(\approx\) 7.7). These cases suggest that, under extreme codec settings or atypical  
Figure 7: Performances of the new ANFIS-VQA model: (**a**) correlation between the user MOS ratings in the LIVE-YT-HFR database and the predicted MOS for 480 video sequences; (**b**) top five test sequences with the largest prediction error (on top of the bars the sequence properties are listed in the format: CFR \(|\) FPS \(|\) SI \(|\) TI). combinations of spatial/temporal statistics, the model's generalization is challenged. In parallel, we measured a rule-activation sparsity by counting, for each of the 480 samples, how many of the seven rules had non-negligible firing strength (threshold > 0.01). On average, only 4.76 \(\pm\) 1.36 rules are active per prediction, demonstrating that the inference remains sparse and that most outputs are driven by a small subset of the rule base. This sparsity underpins the model's computational efficiency and interpretability. Even though seven rules exist, each prediction typically engages fewer than five, reducing evaluation overhead while preserving predictive power. Figure 8 presents the RMSE for each of the evaluated VQA models, except the AVQBits for which the RMSE values were not reported in the original research [23]. The two best-performing methods, i.e., the new ANFIS-VQA (2.9091) and FLAME-VQA (2.9598), stand out with significantly lower RMSE scores compared to all other approaches. Their close scores suggest that both fuzzy logic-based frameworks successfully capture key perceptual factors, with the new ANFIS-VQA demonstrating competitive accuracy while potentially offering advantages in rule generation and interpretability. By contrast, two BH-VQA versions (6.303 and 7.401 for ImgNet and LSVQ, respectively), GSTI (7.422) and VMAF (8.587), register moderately higher RMSE, indicating they still struggle with certain aspects of the dataset's content. Traditional or less perceptually driven metrics such as PSNR (9.023), ST-RRED (10.431), FSIM (10.502), and MS-SSIM (10.726) exhibit larger deviations from subjective ratings, reflecting limitations in capturing motion- and detail-related distortions. Finally, FRQM (10.804), SSIM (10.819), SpEED (10.866), and DeepVQA (11.441) occupy the higher end of the RMSE spectrum, implying these methods are less aligned with the subjective scores for the database under these conditions. Furthermore, the performances of the VQA models were compared using both PCC and SROCC metrics (Figures 9a and 9b, respectively). The top performers, the new ANFIS-VQA (PCC = 0.9174, SROCC = 0.9048) and FLAME-VQA (PCC = 0.9086, SROCC = 0.8961), demonstrate the strongest correlations with user ratings.