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Results presented show that the proposed method significantly outperform all baseline methods in the means of both, the PSNR and the SSIM measure.
It can be seen that the values of PSNR and SSIM are consistent with the visual results, and that the proposed method significantly outperforms all baseline methods in the means of PSNR and specially SSIM measure.
The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging.
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This is why the curve of the baseline method in the figure increases monotonically.
It was also confirmed that the proposed method maintains the LDR image quality and coding performance of the baseline method in the base layer for both the OpenEXR and RGBE formats.
It is interesting to observe that the baseline method achieves only poor values here, whereas the sophisticated module finder MCL, which also operates on interaction network data only, achieves superior values in these novel categories while being at most on a par with the baseline method in the standard values.
In addition, a single-stage SVM classifier, which classifies among all six categories in one step, was also implemented; this served as the baseline method in the study.
NB was included as a baseline method in this study as implemented in the Scikit-learn library utilizing the "BernoulliNB" function [57].
Figure 10 presents the SDRs comparison between the proposed system and the baseline methods in room C, with the SNR = 10 dB, for the determined and underdetermined cases.
Fig. 10 SDR performance comparison between the proposed system and baseline methods in room C, with the SNR = 10 dB, for the determined and underdetermined cases.
Experimental results on synthetic and real-world networks show that our algorithms outperform the baseline methods, in terms of both effectiveness and efficiency.
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