## 6 Experimental Results and Discussion
### Temporal Recovery Detection Results Using the Proposed Model based on Multifractal Features
This is shown in order to point out that even though video content is very similar, a difference exists between HFR data depending on whether TRR is applied or not. Moreover, the analysis shows that such TRR detection based on multifractal features can be developed. Figure 14: (**a**–**c**) Recovered sequences from 30 fps and corresponding (**d**–**f**) spectra presented with original data, where, in (**f**), temporal recovery is performed using different frequency rates. Figure 13: Points included in cross-validation: (**a**) unlabeled samples and (**b**) weighted k-nearest neighbors (WkNN)-based, distinguishing between TRR and original samples. The comparison between different distance measures showed significant advantage of the Mahalanobis metric (15) compared with other choices. In Table 6, the area under the curve is above 0.86 for different metrics for WkNN. The outcomes of the suggested approachbased on four features for (13)-(15) are given in Table 6, where the significant advantage of Mahalanobis is obtained compared with other distances [83]. Moreover, the performance is examined in the feature domain based on different classifiers, like kNN, decision tree and SVM, based on polynomial kernel functions such as linear, cubic and quadratic, as well as WkNN using the Mahalanobics metric, where a cross-validation procedure is applied. This is presented in Table 7, where recall, precision and accuracy are calculated, showing the highest value of accuracy for the proposed WkNN approach. Similar results are obtained for different 120 fps video data from the reference UVG dataset. For different video content included in the cross-validation, high accuracy is confirmed. Namely, the average for six iterations for the proposed model still gives a high accuracy of 98.1%, as shown in Figure 15b. The obtained experimental results for TRR detection in the performed analysis seem promising, giving an accuracy above 98%. All four features are  
Figure 15: (**a**) Recovered ReadySetGo and (**b**) different number of features included in the cross-validation, taking into account tested classifiers with average performance. selected in the proposed model, where the performance based on fewer features is shown in Figure 15. The proper choice of classifier and selecting the right distance metric are necessary to obtain high accuracy results. Video sequences of different complexities and crf values are tested in the experiment which originated in 2160 p resolution, where only temporal conversion was applied with TRR HEVC detection, mainly focused on 120 fps. Compared with this, in [63], where recovery detection is tested, about 96% accuracy for AVC 60 fps video is reported. Future work should be oriented towards spatial conversions and other combinations. Here, HEVC compressed video content which originated in 2160 p resolution and 120 fps is trained and tested, which is usually carried out for much lower resolution formats and AVC [60; 61; 62; 63]. It is expected that for proper training applied to different content and crf values, the model could be applicable for lower frame rates and lower spatial resolutions. All the experimental analyses were performed on the same platform, and ffmpeg was applied for the decoding tasks. This paper's emphasis is on the effects on LRD and multifractality and not the hardware or power consumption. Generally, playing 4k HFR HEVC may produce freezing. Nevertheless, the method does not require reproduction, and the parsing enables collecting traces in the offline mode with minimal graphic utilization. Even though tools for video reproduction can be used, specific graphical processing acceleration possibilities were not applied, such as Nvidia-GeForce-based ones, and the settings were kept as simple as possible. Here, CPU utilization is about twenty percent without visualization. Also, the abovementioned information does not affect the model. Any visualization increases both CPU and graphics unit utilization.