## 1 Introduction  
Video content evolves to more complex forms of media, with a variety of different combinations of spatial resolution, dynamic range, color range, codecs, containers and frame rates. Among others, it is well known that high-frame-rate (HFR) video content is important for high-quality consummation and especially further relevant video investigation, since transmission bandwidth and storage is affected by new frame rate formats [1]. Generating new video and immersive media comes with a lot of challenges for traditional infrastructures and sharing possibilities, particularly for higher resolutions, like temporal. HFR contributes to the increase in perceived quality, but in most practical applications, video still rarely exceeds 60 frames per second (fps) [1, 2]. Low frame rates (LFR) or standard frame rates (SFR) have become an obvious limitation, especially when it comes to sport, fast action genres (as in cinema and gaming) and immersive virtual reality (VR) and augmented reality (AR) content [2]. This is also recognized in BT.2020 or the ultra-high-definition (UHD) television standard, where up to 100- and 120-frame frequencies are adopted for further exploitation [3]. Moreover, in the Advanced Television Systems Committee (ATSC) 3.0 ecosystem, high or very high frame rates are expected, as well as video services developed by interconnecting 5G communication networks and ATSC 3.0 broadcasting. Currently, in these cases, high-efficiency video coding (HEVC or H.265) is adopted to deal with novel video technology formats like HFR [4; 5; 6]. Progressive formats are accompanied by picture rates with possible dealing with SFR and HFR (like 120 fps) recovery and temporal filtering [5; 6]. Media over Internet Protocol (IP) provides a high level of flexibility, and new broadcast systems are already using IP infrastructures [7]. The infrastructures enable HFR distribution, but dealing with video in such manner also leads to significant changes in bandwidth requirements. Besides future formats and services, compression technologies are of crucial interest [6]. The perceptual quality improvement resulting from HFR is recognized in industrial and academic communities [8]. HFR is preferable for applications in order to enhance the smooth end-user experience and to produce different effects [9; 10]. It is not easy to select and adopt frame rates, since frame frequency changes produce distortions. In typical workflows, the frame rate is decided before any acquisition. This leads to general suggestions that production needs to be as high as possible during the production phase, where end-user deliverables are adopted/modified to final needed frame rates [10]. An increasing number of modern content creator captures show their activities using HFR on social networks and sharing IP platforms [11; 12; 13]. Working with fast-forward video and similar content that is not the result of temporal consecutive frames is especially challenging, since the quality is jointly dependant on frame rate and compression [14]. Video reproductions in HFR have been reported and analyzed in [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. Video conversion, on the other hand, usually means frame rate upscaling or upconversion, often referred to as frame interpolation [24; 25]. One should have in mind that it also comes with compression format [26; 27; 28; 29; 30; 31; 32]. There has been extensive analysis of video tracing by long-range dependency (LRD) and multifractals [33; 34; 35; 36; 37; 38]. One of the most popular tracing tools is the Fast Forward Moving Picture Experts Group (Fast Forward MPEG or ffmpeg) solution [33], while self-similarity is considered as one of the most powerful properties [34], where LRD and multifractals have been used in many applications related to different types of sequences [35], statistical modeling and analysis of video traffic [36; 37] and queuing performance [38].