# FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment
## 3 Dataset Properties
### The Video Sequences  
The database contains a set of 16 uncompressed source videos. This assortment encompassed 11 sequences procured from the Bristol Vision Institute High Frame Rate (HFR) video database [42] (recorded with a RED Epic-X video camera, at 3840 \(\times\) 2160 pixels and 120 frames per second). The Institute's public version of the database contained spatially downsampled videos in 1920 \(\times\) 1080 (HD) YUV 4:2:0 8-bit format, lasting 10 s each. Additionally, five videos with high-motion sports content, captured by the Fox Media Group at 3840 \(\times\) 2160 pixels, YUV 4:2:0 10-bit format, were also included, and each of them had a duration of 6 to 8 s. In Figure 1, we captured frames of each source video to showcase the genres of the sequences, which include sports, scenes from nature or urban areas, interior scenes, etc. To ensure a diverse range of scenes and motions in the selected source sequences, the authors calculated Spatial Information (SI), Temporal Information (TI), and Colorfulness (CF) measures for the videos. However, the results of the SI and TI measurements were not included in the public dataset; hence, we performed those calculations for 480 videos using the MSU Quality Measurement Tool (version 14.1) software package [43].  
To generate test sequences featuring a range of frame rates, the authors employed a frame-dropping technique, sidestepping motion blur and yielding lower frame-rate videos that aligned more closely with their inherent capture rates. Subsequently, they undertook the subsampling of 30 test sequences from each source, across six distinct frame rates: 24, 30, 60, 82, 98, and 120 fps. These sequences were then exposed to five tiers of VP9 compression. Notably, frame-rate values of 82 and 98 were deliberately integrated, notwithstanding their relatively lesser popularity. This inclusion aimed to yield more intricate evaluations of video quality, particularly within the spectrum of 60 fps to 120 fps. This nuanced selection played a pivotal role in refining video quality assessment and consequently fostering the development of a more sophisticated model.  
The compression procedure entailed the utilization of FFmpeg VP9 compression [44], employing single-pass encoding while manipulating five distinct Constant Rate Factor (CRF) values. This manipulation led to the generation of five distinct bit rates. The selection of these five compression tiers for a given source sequence unfolded in an ensuing manner. The primary and fifth tiers corresponded to the lossless (CRF = 0) and maximum (CRF = 63) feasible compression levels attainable within VP9, respectively. Conversely, the three intermediary bit rates were chosen to ensure that compression yielded approximately equitable bit rates across all frame rates, thereby engendering a tangible perceptual distinction among them. To that end, the CRF values for the remaining videos, emanating from the source sequence, were aligned with these stipulated bit rates. Consequently, every source content spawned a suite of 30 test sequences (arising from the multiplication of six frame rates by five compression tiers). This procedure was iterated for every source sequence within the database.  
Note that we accepted the authors' annotations, which impacted the naming convention of the input values of our model (discussed in Section 4.2.1).