## 3 VQA Model Design
### Subtractive Clustering Parameter Selection  
The modeling process begins by generating an initial fuzzy inference system (FIS) with MATLAB's Fuzzy Logic Toolbox (MATLAB version R2024b) [10], where subtractive clustering is first applied to the normalized input space. The purpose of this step is to group similar data points into compact clusters whose centers will become the prototypes of the fuzzy membership functions; each center, therefore, marks a distinct region of the four-dimensional space spanned by compression rate, frame rate, spatial information, and temporal information. Subtractive clustering is well suited to this task because it selects cluster centers purely from the local data-density distribution, automatically determining both the number and the shape of the resulting fuzzy sets [11]. Conceptually, this data-driven separation is analogous to the blind-source-separation strategy used by [PERSON] et al. to suppress range ambiguities in space-borne SAR (Synthetic Aperture Radar) imagery: by first disentangling mixed-signal components, the subsequent processing stages operate on cleaner, better-isolated inputs, leading to improved performance [12]. In the ANFIS framework created in this study, the clustering phase likewise \"decouples\" the strongly coupled video parameters, providing a compact yet expressive foundation on which ANFIS can later learn an adaptive rule base. Key parameters in this process and their significance are as follows. 1. Range of influence (RoI) defines the radius within the normalized data space in which an individual data point influences the \"potential\" of a candidate cluster center. Thus, it determines how far the influence of a potential cluster center is computed. A larger range of influence means that each data point affects a broader area, which can lead to a smaller number of clusters. 2. After the first cluster center is chosen, the potential of all other data points in the vicinity is reduced (\"squashed\") around that center. The squash factor (SF) determines how much the potential is diminished in these regions. A higher squash factor results in a stronger reduction of potential near the selected center, which can prevent overlapping clusters and ensure a clearer separation. 3. The accept ratio (AR) sets a threshold (relative to the potential of the first selected cluster center) above which a candidate is automatically accepted as the new center. 4. In contrast to the accept ratio, the reject ratio (RR) establishes a threshold below which a candidate is automatically rejected. As previously reported, the ANFIS framework was implemented in MATLAB (version R2024b), where the data about the video sequences were randomly divided into 35 distinct partitions. To avoid making an arbitrary choice of subtractive clustering parameters, for every training/validation split generated in the cross-validation procedure, a grid sweep was conducted for RoI \(\in\) {0.5, 0.6, 0.7, 0.8}, SF \(\in\) {1.1, 1.3, 1.5}, AR \(\in\) {0.5, 0.6, 0.7} and RR \(\in\) {0.1, 0.2, 0.3}. It is noteworthy to mention that the RoI values below 0.5 produce small radii and an excessive number of clusters, while values above 0.8 merge dissimilar regions and over-smooth the input space. Furthermore, moderate squash values (1.1-1.5) ensure that neighboring clusters are sufficiently separated, preventing redundant, highly overlapping membership functions, while the chosen AR and RR thresholds filter candidate centers whose potential is either significant (AR) or negligible (RR), stabilizing the rule count and avoiding spurious clusters caused by noise. Thus, the chosen parameter ranges were selected a priori to balance model compactness and descriptive power. This yielded 4 \(\times\) 3 \(\times\) 3 \(\times\) 3 = 108 configurations per split. A total of 3780 ANFIS trainings were executed (35 partitions \(\times\) 108 configurations) for 35 epochs each. The combination RoI = 0.8, SF = 1.1, AR = 0.5, and RR = 0.1 achieved the lowest average RMSE (3.080) and the smallest standard deviation (0.317).