## 9 Conclusions  
The proposed IV-SSIM metric is a successor and potential replacement of our previous work--IV-PSNR [4]--which is a PSNR-based metric adapted for the characteristics of immersive video. IV-SSIM is a full-reference objective quality metric based on a calculation of structural similarity (SSIM) between two images. IV-SSIM combines the advantages of SSIM and IV-PSNR, properly mimicking a human visual system in assessing the quality of immersive video, with its typical distortions caused by reprojection of pixels between multiple views: slight shift of reprojected pixels and color artifacts originated from various color characteristics of different views. The IV-SSIM metric was compared to 16 state-of-the-art metrics in two experiments. In the first one, its efficiency in an immersive video coding scenario was evaluated. In the second experiment, IV-SSIM was evaluated against the TID2013 database [43], showing its performance in a typical non-immersive video application. The experiments have shown that IV-SSIM clearly outperforms state-of-the-art metrics in assessing the quality of immersive video, also being highly competitive for the assessment of various kinds of distortions introduced to typical 2D images. Moreover, the calculation of IV-SSIM is straightforward and parallelizable, making the proposed metric practical and convenient to use in real, practical immersive video systems. Its efficiency was appreciated by the experts of the MPEG Video Coding group, resulting in including IV-SSIM in the common test conditions for MPEG immersive video--MIV CTC [51] and implicit neural visual representation--INVR CTC [52]. The implementation of the IV-SSIM metric was provided by the authors of this paper within the Quality Metrics for Immersive Video (QMIV) software v1.0 [53] and is publicly available on the MPEG Video Coding public git repository: [[https://gitlab.com/mpeg-i-visual/qmix](https://gitlab.com/mpeg-i-visual/qmix)]([https://gitlab.com/mpeg-i-visual/qmix](https://gitlab.com/mpeg-i-visual/qmix)) (accessed on 11 August 2024). Currently, IV-SSIM is based on the default definition of the structural similarity index (SSIM) presented in [11]. However, such a definition (e.g., using the Gaussian weighting mask (\(\omega\)) equal to 11 \(\times\) 11) is not efficient in terms of computational complexity. On the other hand, several more efficient implementations of SSIM are known and commonly  
Figure 6: SROCC and computational time for all considered metrics; immersive video coding scenario. Calculations were performed on AMD Ryzen 9 3900 XT (12 cores). used, e.g., the rectangular mask used in the ffmpeg framework [54]. Therefore, in the future, different implementations of the structural similarity in the IV-SSIM metric should be analyzed. Moreover, similarly to SSIM, the proposed IV-SSIM lacks temporal video stability analysis. In the vast majority of scenarios, rendering-induced distortions in immersive video dominate over temporal distortions. However, in some cases (e.g., significant scene geometry changes over a short period of time, resulting in dramatically unstable depth maps), the metric may be insensitive to video flickering, which decreases the subjective quality of video. Therefore, the second direction for future work is to increase the efficiency of IV-SSIM by implementing temporal consistency analysis. Conceptualization, A.D. and J.S. ; methodology, A.D.; software, J.S. ; validation, A.D. and W.N. ; writing, A.D. and J.S. All authors have read and agreed to the published version of the manuscript. The research was supported by the Ministry of Science and Higher Education of the Republic of Poland. Not applicable. The experiment described in Section 7 was conducted using the publicly available dataset TID2013 [43], [[https://www.ponomarenko.info/tid2013.htm](https://www.ponomarenko.info/tid2013.htm)]([https://www.ponomarenko.info/tid2013.htm](https://www.ponomarenko.info/tid2013.htm)), accessed on 11 August 2024.