## 6 Experimental Results and Discussion
### Temporal Recovery Detection Results Using the Proposed Model based on Multifractal Features  
It is of practical importance to distinguish TRR from original HFR video data. Simply reducing and recovering HFR can be considered as one of the typical procedures in order to compensate found losses and to match original frequency. Nevertheless, it should not be neglected that distortions exist and need not be replaced with original data in cases where media integrity and authentication are involved. This should be available for any quality of level expressed here through compression. The selected crf mode enables a practical and sophisticated approach compared with setting constant quantization or output quality level. So, such settings are beneficial in practice, since they lead to results that are dependable on video content itself. The functional TRR detection approach presented in Figure 5b should serve a wide purpose as a tool. Here, UHD 120 fps data are analyzed. After preparing video data and generating video trace sequence, feature extraction is performed according to four multifractal features that can be noted as feature1 to feature4, respectively, they are relative width, relative height, right side slope of multifractal spectra and left side distance based on two characteristic points of a spectrum. This is followed by a classifier which employs features that do not apply any reference. The performance of different classifiers in multifractal TRR detection is calculated using five randomly selected video source files from Table 1. These files give two groups of samples by using six crf values. In five-fold cross-validation, totally, sixty samples are used, presented in Figure 13a, where videos numerated from 1 to 5 are included. In Figure 13a, samples are presented as points in two feature domains. There is a linear trend of slope 1.07, and the norm of residuals calculated as the square root of the sum of the squares is 0.037. By examining cases, linear fits can be made for each group here. Namely, each group has similar norm of residuals close to 0.021 but with similar regression slopes close to 1.07, implying that appropriate fitting lines can be considered as parallel. The proposed model for temporal recovery detection is based on WkNN using Mahalanobis metric. In Figure 13b, a cross-validation result is presented, where one point is misdetected as temporally recovered. In this case, this is a point corresponding to the Beauty sequence of crf20. The trends of samples for each of the group are noticeable, even though different content is included in the tests. The experiment is performed on data recovered from 60 fps. Additional tests in practice were conduced in order to recover HFR HEVC data from 30 fps, showing satisfying results, as shown in Figure 14.  
This is confirmed for YachtRide numerated as the sixth source video from [23] and TRR from 30 fps, which was not included in the cross-validation. Multifractal spectra of TRR data from different frame rates, like 24 fps, 30 fps, 60 fps and 96 fps, are presented in Figure 14f. Even though all YachtRide recovered data are correctly detected, one should be aware that cross-validation is performed only on TRR from 60 fps and original data of 120 fps, and that such cases with different downsampling should probably be included in the training. Future experiments need to include various combinations in the spatial-temporal domain, which was not the focus of this paper. Here, it was of interest to show that the multifractal domain and the proposed TRR detection can show high accuracy detection percentage in the set experiment using a relatively low number of features. In Figure 15a, recovered ReadySetGo is presented using several successive frames, obtaining recovered 120 fps suitable for comparing with 120 fps original data, where the difference between these two 120 fps is not easy to observe by a standard viewer.