## 5 Experimental Results and Analysis  
In this section, our experimental results and analysis are presented using four common benchmark IQA databases: KADID-10k [20], TID2013 [7], TID2008 [21], and CSIQ [8]. More specifically, the proposed _SG-ESSIM_ method was compared to thirteen other state-of-the-art FR-IQA metrics (2 stepQA [50], CSV [51], DISTS [52], ESSIM [19], GSM [29], IW-SSIM [16], MAD [8], MS-SSIM [26], PSNR, ReSIFT [53], RVSIM [54], SSIM [23], SUM-MER [55]), whose original source codes were made publicly available by their authors, on the above-mentioned databases in terms of correlations with ground truth quality ratings. Furthermore, their hyperparameter values (if any) were applied in the examined state-of-the-art methods, which were recommended as default values by the original authors in their MATLAB implementations. In addition to this, a detailed comparison of TID2013 [7] and TID2008 [21] with respect to different noise types and noise levels is also presented. The comparison, in terms of the PLCC, SROCC, and KROCC, in KADID-10k [20], TID2013 [7], TID2008 [21], and CSIQ [8] is summarized in Tables 3 and 4. From these experimental, numerical results, it could be seen that the proposed _SG-ESSIM_ was able to provide the best results in terms of the SROCC and KROCC on KADID-10k [20] and the PLCC on TID2013 [7], respectively. Moreover, it gave the best results for all correlation values in TID2008 [21]. On CSIQ [8], the second best results in terms of the SROCC and KROCC were achieved by the proposed method. To illustrate the overall performance of the examined methods on these databases, direct and weighted average (using the numbers of images as weights) performance values are summarized in Table 5. It could be seen that the proposed method was able to deliver the second best results in terms of the SROCC and KROCC, both in direct and weighted averages. In Tables 6 and 7, detailed results could be seen on TID2013 [7] and TID2008 [21] with respect to the different distortion types. Similarly, Tables 8 and 9 illustrate the detailed results with respect to the noise levels. TID2013 [7] contained images with 24 different distortion types, such as AGN (additive Gaussian noise), ANC (additive noise in color components), SCN (spatially correlated noise), MN (masked noise), HFN (high frequency noise), IN (impulse noise), QN (quantization noise), GB (Gaussian blur), DEN (image denoising), JPEG (JPEG compression), JP2K (JPEG2000 compression), JGTE (JPEG transmission errors), JZTE (JPEG2000 transmission errors), NEPN (noneccentricity pattern noise), BLOCK (local block-wise distortions of different intensity), MS (mean shift), CC (contrast change), CCS (change in color saturation), MGN (multiplicative Gaussian noise), CN (comfort noise), LCNI (lossy compression of noisy images), ICQD (image color quantization with dither), CA (chromatic aberrations), and SSR (sparse sampling and reconstruction). TID2008 [21] consisted of the first 17 distortion types from TID2013 [7]. From the presented results, it could be observed that _SG-ESSIM_ was able to produce the best SROCC values on five distortion types of TID2013 [7] and on four distortion types of TID2008 [21]. On the other hand, it provided the second best results on nine distortion types of TID2013 [7] and on seven distortion types of TID2008 [21]. Moreover, the _SG-ESSIM_ was able to produce the best SROCC values on three distortion levels of TID2013 [7], while it gave the second best results on the remaining two distortion levels. In TID2008 [21], it provided the best results on three distortion levels and the second best result on the remaining distortion level. \begin{table}
\begin{tabular}{c c} \hline \hline Computer model & STRIX Z270H Gaming \\ Operating system & Windows 10 \\ Memory & 15 GB \\ CPU & Intel(R) Core(TM) i7-7700K CPU 4.20 GHz (8 cores) \\ GPU & Nvidia GeForce GTX 1080 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computer configuration applied in our experiments.