## 6 Objective QoE Evaluation of Cloud Gaming
### Correlation of FR and NR Metrics with Subjective QoE  
Next, Pearson Correlation Coefficient (PCC) and Spearmans' Rank Correlation Coefficient (SCC) values were determined for all three FR measures versus the subjective QoEs obtained in the preceding section. PCC measures the linear relationship between two variables on a scale from \(-1\) to 1, with 0 indicating no correlation and 1 indicating a perfect positive linear relationship. However, PCC cannot show good relationships for variables which are non-linear. SCC, on the other hand, can indicate a nonlinear relationship between two variables. Similar to PCC, this also gives a value from \(-1\) to \(+1\). It can determine the relationship between two variables, whether they are continuous or ordinal (like ACR). The findings of PCC and SCC in both correlated and non-correlated cases are summarised in Table 2. The \(p\) values for the Pearson correlation were not included in the table as they were all less than 0.05, indicating that the correlation coefficient \(r\) was statistically significant. In the majority of cases, SSIM and PSNR exhibited a greater correlation coefficient with SCC than PCC, as shown in Table 2. Both SSIM and PSNR are non-linear FR measurements; therefore, this was to be expected. VMAF, ranging from 0 to 100, exhibited greater coefficient values with PCC than with SCC. This again was expected, since VMAF rating was developed to represent user experience in a linear scale. Secondly, the VMAF performed the best among all FR metrics across all three games. It was also the most consistent of the three, which was understandable, considering that it was designed primarily to evaluate the human perspective of online streaming videos. SSIM and PSNR were primarily developed to determine image quality, not user experience. So, in some cases, such as non-correlated FIFA and CSGO scenarios, it was able to perform well. In other circumstances, though, it was incapable of representing the human perspective of the game experience. Finally, all three FR metrics performed worse in correlated \"Delay-Loss\" scenarios compared to non-correlated scenarios. Only VMAF was able to show some level of consistency in the correlated scenario. Despite being a full-reference VQA metric, its performance was still inadequate in determining user QoE with a high level of accuracy. This showed how the VMAF was developed and trained in non-correlated QoS scenarios. SSIM was able to achieve some level of consistency with slower-paced games, but was extremely poor with the fast-paced GTA. PSNR was the worst out of all three FR metrics showing very inconsistent results, especially with FIFA and GTA games. This again showed the weaknesses of the existing VQA metrics in predicting the human user QoE, especially for cloud gaming scenarios, with the cross-correlated nature of the real internet. Now, the issue with online applications is that often FR metrics are not a very useful method for QoE analysis. This is because it is difficult to obtain both server-side and client-side videos, let alone ensure the frames of the videos are in sync to carry out the FR metrics mentioned above. As a result, No-Reference (NR) metrics are becoming more and more popular among the industry and academics. Quite a few such NR metrics have developed, such as the Blind/Referenceles Images Spatial Quality Evaluator (BRISQUE) and the Natural Image Quality Evaluator (NIQE). However, in our previous work [15; 44], we showed how these NR metrics were unable to perform consistently for cloud gaming in a non-correlated QoS setting. In that work, we proposed an alternative NR model, that made use of a fusion of perceptual hash (pHash) and SSIM, to identify duplicated packets. We chose to utilise duplicated packets because, first, they can be retrieved from client-side video alone, without the necessity for server-side video, and, secondly, the number of duplicated packets is highly correlated with PLR in cloud gaming.