## 2 HFR Processing and Challenges  
It is well known that frame rate impacts quality of experience related to how realistic content being consumed is or which style one desires to obtain, like motion blur, slow motion or fast forward. HFR video is expected to approach realism when there are a lot of actions happening, as in sports, busy scenes in movies and gaming but also in live and realistic experience with crisp information. One of the benefits is increased realism, where video seems more immersive by making the viewer's experience more lifelike. Frame rate is generally described as number of frames per second (fps), which is illustrated in Figure 1, where HFR means that temporal resolution is increased and more images are captured in a given amount of time. HFR can be described as video content captured or displayed at a frame rate of 60 fps or higher. This is in contrast to the SFR/LFR that is typically used for television [5.6]. Besides the realistic experience which is tied to the perception of motion, HFR reduces motion blur when an object is moving and enables its clearer representation, described as smooth motion given in more detail. Even so, differences in frame rates in acquisition and reproduction may produce uneven pacing and sometimes longer frames. Inconsistent frame times, known as judder, and decreasing low frame rate producing so-called stutter effects are only some of the issues [12]. Video quality estimation may produce different results due to varying video behavior when transforms are made in frame rates according to specific quality mode selections. Wearable and lightweight cameras like action cameras are popular in the consumer industry, meaning that both professional and nonprofessional content has a variety of distortions [13]. Temporal resolution changes through objective measurements are still mainly analyzed using standard full-reference approaches [17]. Quality assessments are usually made by mean squared error, peak signal-to-noise ratio, structural similarity index, video multimethod assessment fusion and similar metrics [18; 19; 20]. In [12], frame rate differences have been considered by using video multimethod assessment fusion (VMAF) and entropy differences. Video collections like Youtube-UGC [21] or Konstanz KoNViD-1k [22] are made for research using different quality scores, compression results and distortion diversity, which can be used for purposes like constructing general no-reference models. Still, a few studies have been specifically considering HFR with publicly video sets that are acquired with frame rates equal to or above 60 fps for research reasons, like Waterloo HFR [16], LIVE-YT-HFR [12] and BVI-HVR [1]. Primarily, for HFR experimental analysis, the video tracing and compression analysis Ultra Video Group (UVG) dataset can be used, since it consisted of 120 fps sequences of even higher spatial resolution, meaning containing RAW video content up to 4k 120 fps [23]. This is the reason why this dataset is chosen here. The general suggestion in production is to keep the frame rate high as much as possible, where the choice of video frame rate may be intentionally HFR [10]. One should have in mind that even if an acquired video is HFR, this can be a significant barrier for many systems and devices. Devices with limited processing power may require downsampling, like frame dropping. HFR may be experienced even as unnatural and not easy to follow by the human visual system, leading to frame rates being decreased. Moreover, interoperability between components of a system may cause lower frame rates for HFR processing tasks. Frame rate can be downscaled, leading to significant decrease in cost in storage or streaming. On the other hand, it is well known that decreasing the frame rate can also result in choppy video experience. This means that video conversion can also be followed by frame rate upscaling, usually referred as frame interpolation, where temporal resolution is increased by adding the frames between the known frames [24; 25].