## 4 HFR HEVC Video Traces and Temporal Recovery Data  
Temporal recovery or frame upconversion detection has been examined in [60; 61; 62; 63] using MPEG-4/AVC traces. In [62], motion-compensated frame rate upconversion is proposed with the possibility of its detection via optical flow algorithm, where original frame rates were of 15 fps. A frame rate conversion detection is also analyzed in [63], having in mind interpolation schemes like common nearest neighbor interpolation. An automatic approach using machine learning is proposed for four original frame rates of 15 to 30 fps and conversions up to 30 fps. Multifractality may be useful in recovery detection, having in mind video tracing and multifractal analysis [56; 57; 58; 59; 64; 65; 66]. For example, machine learning and multifractal features are applied for an intrusion detection system in an unmanned aerial system in [65]. Moreover, Legendre multifractal spectrum is applied in [66] foranimation frame analysis and its differentiation from real and partially animated ones, especially due to self-similarity properties found also in video traffic analysis. In this paper multifractal analysis of HFR frame size traces of HEVC compressed video sequences is the focus. LRD and self-similarity effects are considered for compressed video characterization, with special attention to their application in video change/modification detection. Here, HFR video traces are collected similarly to as is explained in the previous section, where frame size sequences are extracted using an accompanied parser [57]. HEVC compressed video represents input for the parser, which is applied for obtaining the xml trace file needed for statistical analysis, as shown in Figure 3.  
Video trace sequences are generated using ffmpeg v5.1.2 for different content. If audio exists within a file, it is removed. Constant rate factor, denoted as crf, i.e., two-pass crf, is selected as a model for controlling the output. The crf option is available for popular codecs and keeps the output quality level by rate control method, which is applied in practical implementations. Lower crf values in compressed data correspond to higher video quality. Six crf values are used, ranging here from 20 to 40. The supported preset option focused on speed and codec complexity is set to default, meaning medium, for the video trace collection, and no additional tuning is applied. The trace/data collection and the experimental analysis are carried out on Intel(R) Core(TM) i7-10750H Central Processing Unit (CPU), 2.60 GHz with 16 GB Random-Access Memory (RAM) on Windows 10 Pro 64 bit operating system without including specific graphical acceleration possibilities. Since it is of interest to investigate the behavior of video traces, the testing circumstances are selected to be as simple as possible. In order to perform the analysis, the experimental procedure employs LRD and multifractal methods for estimation of HFR. The most common HFR video change is temporal resolution recovery, named here as TRR. The scenario of typical TRR found in practice includes temporal filtering, followed by temporal resolution matching. Significant savings in memory and channel capacity can be probably temporarily made in the temporal domain by decreasing frame frequency, and this is called temporal filtering [5; 6]. Frame frequency alignment leads to HFR TRR. It is generated by the increase in frame number after a loss of original data, where specific temporal upsampling is ignored, as in [67], to avoid the choice of different methods and adding undesirable artifacts. Here, it is assumed that self-similarity properties of video traces may be observed in TRR scenario. TRR is valuable, since the practical implementations often need savings and further comparisons in the HFR domain. This HFR TRR after temporal filtering can be considered common in practices where it is needed to have a matching frame rate, as in the original HFR video case. In this paper, the focus in on differentiating these TRR and original sequences.