# Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation
## 4 Experiments and Results
### Performance Comparison with 2D and 3D-IQA Metrics  
To evaluate the effectiveness of the proposed method, we compared its performance with various existing 2D and 3D-IQA metrics. We compared the performance of the proposed metric with widely used 2D quality assessment metrics: PSNR, SSIM [60], VSNR [74],IFC [75], MSSIM [29], VIF [76], and UQI [77]. Before computing the performance parameters, the objective scores computed by these metrics were also mapped to MOS values by using the same logistic function mentioned in Equation (12). In all experiments, the implementation of the method provided by the authors or other parties was used. The comparison in terms of all five performance parameters is presented in Table 1. The best results are highlighted in bold for convenience. The results reveal that the proposed algorithm outperforms all the compared 2D-IQA algorithms in all performance parameters. Specifically, the proposed method achieves PLCC 0.8909, SROCC 0.8979, and KROCC 0.7095 with minimum RMSE 1.1816. We also evaluated the performance the proposed metric with thirteen well-known and recent 3D image quality assessment metrics. They include 3 DSwIM [55], StSD [78], Chen [25], Benoit [79], Campisi [21], Ryu [50], PQM [80], [PERSON] [36], [PERSON]\({}_{l}\), [PERSON]\({}_{g}\)[19], SIQM [73], NIQSV [81], and ST-SIAQ [82]. The evaluation results presented in Table 2 show that the proposed method outperforms all the compared methods in each performance parameter and achieves the best PLCC (0.8909) with the minimum RMSE (1.1816). The other measures, SROCC, KROCC, and MAE, also reveal that the proposed method performs better than other 3D-IQA metrics. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **PLCC** & **SROCC** & **KROCC** & **RMSE** & **MAE** \\ \hline PSNR & 0.8226 & 0.8400 & 0.6393 & 1.4794 & 1.1830 \\ SSIM & 0.7656 & 0.7777 & 0.5796 & 1.6737 & 1.2810 \\ VSNR & 0.8287 & 0.8368 & 0.6357 & 1.4560 & 1.1520 \\ IFC & 0.5104 & 0.7423 & 0.5495 & 2.2376 & 1.8331 \\ MSSIM & 0.8666 & 0.8762 & 0.6852 & 1.2983 & 1.0188 \\ VIF & 0.7983 & 0.7933 & 0.5999 & 1.5668 & 1.2240 \\ UQI & 0.7537 & 0.7564 & 0.5595 & 1.7099 & 1.3066 \\ Ours & **0.8909** & **0.8979** & **0.7095** & **1.1816** & **0.9245** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall performance comparison of the proposed and the compared 2D image quality assessment metrics on MCL-3D dataset. The best results are highlighted in bold. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **PLCC** & **SROCC** & **KROCC** & **RMSE** & **MAE** \\ \hline
3 DSwIM & 0.6497 & 0.5683 & 0.4088 & 1.9777 & 1.4902 \\ StSD & 0.6995 & 0.7008 & 0.5084 & 1.8593 & 1.4563 \\ Chen & 0.8615 & 0.8708 & 0.6769 & 1.3212 & 1.0362 \\ Benoit & 0.7425 & 0.7518 & 0.5589 & 1.7429 & 1.3480 \\ Campisi & 0.7656 & 0.7777 & 0.5796 & 1.6737 & 1.2810 \\ PQM & 0.8612 & 0.8646 & 0.6729 & 1.3225 & 1.0029 \\ Gorley & 0.7099 & 0.7196 & 0.5270 & 1.8323 & 1.4749 \\ You\({}_{l}\) & 0.7504 & 0.7567 & 0.5600 & 1.7196 & 1.3325 \\ SIQM & 0.7744 & 0.7756 & 0.5648 & 1.6461 & 1.2757 \\ You\({}_{g}\) & 0.3650 & 0.6609 & 0.4938 & 2.4222 & 2.0123 \\ Ryu & 0.8752 & 0.8824 & 0.6923 & 1.2584 & 0.9859 \\ NIQSV & 0.6783 & 0.6208 & 0.4384 & 1.9118 & 1.6032 \\ ST-SIAQ & 0.7133 & 0.7034 & 0.5118 & 1.8233 & 1.4028 \\ Ours & **0.8909** & **0.8979** & **0.7095** & **1.1816** & **0.9245** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall performance comparison of the proposed and the compared 3D image quality assessment metrics on MCL-3D dataset. The best results are highlighted in bold. To further investigate the effectiveness of the proposed method, its performance with the compared 2D and 3D-IQA metrics was also evaluated for individual distortion types, i.e., AWN, Gauss, Sample, Transloss, JPEG, and JP2K. Recall that the stereopair images in the dataset were generated through DIBR from the polluted depth and/or the color images with these types of noise. The results of the comparison with 2D and 3D quality metrics in terms of PLCC are reported in Tables 3 and 4.