## 6 Effectiveness in Immersive Video Coding  
In the first experiment, IV-SSIM was evaluated using the results of the \"MPEG Call for Proposals on 3 DoF+ Visual\" [47], which contain videos compressed using seven different immersive video coding techniques, five test sequences (both natural and computer-generated), and four rate points (6.5, 10, 15, and 25 Mbit/s). The subjective quality evaluation was performed using the Double Stimulus Impairment Scale test method [48] on a group of 18 naive viewers [49]. The participants assessed the quality of the \"pose trace\" videos, simulating the virtual navigation of the viewer within a scene [50]. The objective quality was evaluated by assessing the quality of synthesized input views (virtual views rendered in the position of real cameras that captured the scene). A detailed methodology for subjective and objective evaluation of the immersive video coding dataset can be found in the publicly available ISO/IEC MPEG document [47].  
The dataset was used to evaluate a correlation between MOS (mean opinion score) and 17 objective quality metrics. The results are presented in Figure 4. As shown, IV-SSIM achieved the highest SROCC and PLCC among all 17 tested metrics. It outperformed the second-best metric--IV-PSNR--by 0.065 and 0.068 for SROCC and PLCC, respectively. When compared to other metrics, the difference is even higher, showing the superiority of IV-SSIM in the assessment of immersive video.  
The difference between correlation coefficients measured for IV-SSIM and SSIM\({}_{\rm YUV}\) (three-component SSIM, not adapted to immersive video) is 0.205 for SROCC and 0.218 for PLCC (cf. Table 1). A similar difference was measured for IV-PSNR and PSNR\({}_{\rm YUV}\) (0.201 and 0.211). Such a result shows that the handling of typical immersive video distortions increases the correlation between objective and subjective quality in a consistent way, independently of the objective metric, which was used as a basis (PSNR or SSIM).  
Despite the fact that the proposed IV-SSIM metric does not assess the temporal consistency of video, it significantly outperforms other metrics, including the ones that measure  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & **Correlation Coefficient** & \multirow{2}{*}{**PSNR**} & \multirow{2}{*}{**IV-PSNR [4]**} & \multirow{2}{*}{**SSIM [11]**} & \multirow{2}{*}{**IV-SSIM**} \\  & **Coefficient** & & & & \\ \hline immersive video coding & SROCC & 0.527 & 0.728 & 0.588 & 0.793 \\ (Section 6) & PLCC & 0.520 & 0.731 & 0.581 & 0.799 \\ \hline general applications—TID2013 & SROCC & 0.818 & 0.825 & 0.839 & 0.850 \\ (Section 7) & PLCC & 0.816 & 0.820 & 0.823 & 0.825 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between the proposed IV-SSIM metric, typical SSIM [11], IV-PSNR [4], and PSNR. Three-component versions of PSNR and SSIM (i.e., PSNR\({}_{\rm YUV}\) and SSIM\({}_{\rm YUV}\)) were used.  
the temporal similarity (e.g., VMAF). The reason for such a behavior is, as mentioned in Section 3, an atypical characteristic of immersive video, where temporal distortions are introduced not by the timeline, but by changing the viewpoint.