# Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation
## 4 Experiments and Results
### Variance of the Residual Analysis  
Variance is the squared deviation of a measure from its mean. It is generally used in evaluating the efficiency of an image quality assessment metric by finding how much the scores computed by an objective IQA metric are closer to the subjective scores. This is achieved by computing the difference between the predicted scores and actual scores. A small difference indicates that the results computed by the metric are reliable and close  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Metric** & **AWN** & **Gauss** & **Sample** & **Tansloss** & **JPEG** & **JP2K** \\ \hline
3 DSwIM & 0.4640 & 0.8218 & 0.8127 & 0.7566 & 0.6431 & 0.6478 \\ StSD & 0.7472 & 0.8429 & 0.8392 & 0.6527 & 0.7372 & 0.7836 \\ Chen & 0.9058 & 0.8978 & 0.9088 & **0.8842** & 0.9078 & 0.8928 \\ Benoit & 0.9102 & 0.8600 & 0.8544 & 0.6796 & 0.8044 & 0.8064 \\ Campisi & 0.8652 & 0.8594 & 0.8535 & 0.8542 & 0.7849 & 0.8052 \\ Ryu & **0.9343** & **0.9409** & 0.9163 & 0.8685 & 0.8935 & 0.9284 \\ PQM & 0.8653 & 0.9346 & 0.9337 & 0.8052 & 0.8732 & 0.9027 \\ Gorley & 0.7735 & 0.8550 & 0.8544 & 0.6043 & 0.8326 & 0.9051 \\ You\({}_{l}\) & 0.9278 & 0.8560 & 0.8564 & 0.7930 & 0.7934 & 0.8071 \\ SIQM & 0.7798 & 0.8738 & 0.8673 & 0.6851 & 0.8858 & 0.7057 \\ NIQSV & 0.8184 & 0.9156 & 0.8862 & 0.719 & 0.2798 & 0.7070 \\ ST\_SIAQ & 0.7884 & 0.8215 & 0.8070 & 0.8264 & 0.742 & 0.7884 \\ You\({}_{g}\) & 0.8856 & 0.8685 & 0.7138 & 0.5373 & 0.8599 & 0.9121 \\ Ours & 0.9081 & 0.9332 & **0.9370** & 0.8195 & **0.9502** & **0.9369** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the proposed and the compared 3D image quality assessment (3D-IQA) metrics on individual distortion types in terms of PLCC. The best results are highlighted in bold.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Metric** & **AWN** & **Gauss** & **Sample** & **Tansloss** & **JPEG** & **JP2K** \\ \hline PSNR & 0.9125 & 0.9047 & 0.9051 & 0.8602 & 0.7491 & 0.8719 \\ SSIM & 0.8652 & 0.8594 & 0.8535 & 0.8542 & 0.7849 & 0.8052 \\ VSNR & 0.8943 & 0.8533 & 0.8449 & 0.7571 & 0.7352 & 0.6150 \\ IFC & 0.9114 & 0.7116 & 0.7733 & 0.6004 & 0.8394 & 0.5985 \\ MSSIM & 0.9103 & 0.9077 & 0.9106 & **0.9011** & 0.9097 & 0.8963 \\ VIF & **0.9555** & 0.9285 & 0.9128 & 0.8168 & 0.9417 & 0.9155 \\ UQI & 0.8501 & 0.8669 & 0.8608 & 0.8494 & 0.8896 & 0.7537 \\ Ours & 0.9081 & **0.9332** & **0.9310** & 0.8195 & **0.9502** & **0.9369** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the proposed and the compared 2D image quality assessment (2D-IQA) metrics on individual distortion types in terms of Pearson linear correlation coefficient (PLCC). The best results are highlighted in bold.  
to the actual scores. To compute this variance \(\sigma^{2}\), first the residual difference between the DMOS and predicted scores after non-linear mapping (DMOS\({}_{p}\)) is computed:  
\[\widehat{R}(i)=DMOS_{p}(i)-DMOS(i) \tag{18}\]  
The variance \(\sigma^{2}\) of each compared 2D and 3D quality metric was computed from its residuals \(\widehat{R}\) and the statistics are presented in Table 5. The results show that our method achieves the smallest variance among all compared methods, which means the scores estimated by the proposed method are highly correlated with the subjective ratings.