# FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment
## 3 Dataset Properties
### The Subjective Experiment  
The evaluation of subjective video quality for the prepared sequences utilized the Single-Stimulus Continuous Quality Evaluation (SSCQE) method, as outlined in [45]. Following the details presented in [9], a total of 85 volunteer undergraduate participants were engaged from the authors' university. This group consisted of 14 females and 71 males, spanning an age range of 20 to 30 years. All participants possessed regular or corrected-to-normal color vision. Each participant evaluated a set of sequences in the same viewing conditions. The sequences were played on a 27-inch screen (3840 \(\times\) 2160 pixels), using Venueplayer, and the viewing distance was set to 30 inches, (around 76 cm).  
To mitigate potential biases, the video database underwent division into four distinct subsets, each encompassing 120 videos. Every participant assessed two out of the four sets across two sessions, thereby resulting in each subject evaluating 240 videos. For the sake of introducing randomness, playlists were curated for each participant by subjecting the 120 sequences to a randomized reordering process. This strategy ensured that sequential videos originated from distinct source sequences and frame rates, thereby effectively minimizing the influence of contextual and memory biases in the assessment of subjective quality. Furthermore, to prevent any prejudicial impact related to viewing order, unique  
Figure 1: Extracted frames from the sequences of the LIVE-YT-HFR database: **(a)** a person running; **(b)** three persons running; **(c)** a person doing flips; **(d)** runners jumping hurdles; **(e)** a long-jump video; **(f)** a bobblehead spinning; **(g)** a shelf filled with books; **(h)** underwater ball bounce; **(i)** two persons passing a ball; **(j)** a street cyclist; **(k)** a hamster running a wheel; **(l)** a lamppost in a park; **(m)** falling leaves; **(n)** a spinning top; **(o)** underwater bubbles; **(p)** water waves splashing.  
playlists were individually tailored for each participant within every session. This measure was adopted to avert any potential bias arising from the sequence in which videos were presented.  
During the evaluation process, an interactive continuous quality rating scale was presented on-screen following the conclusion of each video. The scale comprised five Likert indicators, ranging from bad to excellent, providing the participants with a guided framework for their rating task. Participants were explicitly instructed to evaluate the perceived quality of the content while disregarding any personal preferences or content-related interests. To combat potential subjective fatigue, a minimum interval of 24 h was mandated between successive rating sessions. The duration for each session was kept within 40 min, and each video was rated by a minimum of 42 users to ensure sufficient data for analysis. To validate the reliability of the subjects' ratings, the authors undertook supplementary analyses to evaluate both inter-subject and intra-subject consistency.  
In their work, the authors analyzed the obtained results from the subjective study, for instance, calculating MOS for each video sequence. But in our analysis, we use the raw data published on the GitHub page of the authors' project (the link to the page is published also in [12]). Note that we divided the dataset randomly into two subsets. One was used for model training and the other for testing. The ratio between the training and test data subsets was 80:20, respectively, i.e., the ratings of the 68 test subjects were used for the model training, and the rest (17) were used for model testing. As discussed earlier, each test subject rated 240 videos, meaning both of our data subsets contained the ratings for every video in the database.