## 4 Experimental Results

Table 4: Brightness regression model summary. Figure 10: Standardized regression residuals of normal P-P diagram. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & **Sum of Squares** & **Degrees of Freedom** & **Mean Square** & **F Value** \\ \hline Return & 5382.379 & 10 & 538.238 & 10200.217 \\ Residual & 55.617 & 1054 & 0.053 \\ Total & 5437.996 & 1064 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Analysis of variance (ANOVA). The statistical coefficients of the linear regression model are presented in Table 6. Note that mode switching between 2D and 3D images has the most significant impact on the screen image brightness. That is, once the screen is switched from the 2D to 3D mode, the image brightness on the screen exhibits a very significant decline. Each of the independent variables is explained and analyzed below. (1) Screen size  
After the 2D/3D mode variable, the screen size is the most important environmental variable when measuring brightness in the darkroom. The linear regression model assumes three screen sizes as separate dummy variables. The statistical results show a significant impact on the brightness, and larger screens have a positive effect on the brightness value, falling within the range of reasonable consideration. Therefore, the screen size and interactions between other factors significantly affect the brightness. (2) Scene recording brightness  
Only two brightness values were used in this study: 3200 and 7500 lx. The statistical results show that these two variables are within the range of reasonable consideration. (3) Mode (2D or 3D)  
This variable is the core consideration of the experiment, and its significance is apparent in the results of the statistical analyses. Thus, this statistical coefficient affects the screen image brightness very significantly. The experimental measurements and the linear regression model prove that the 3D image brightness is lower than that of the 2D images. To estimate the screen image brightness value, the linear regression model takes the 2D and 3D modes as dummy variables. The expected screen display mode is set in the linear regression model so that the estimated image brightness value can be obtained. \begin{table}
\begin{tabular}{l c c c c} \hline  & \multicolumn{2}{c}{**Non-Standardized**} & \multicolumn{2}{c}{**Standardized**} \\
**Brightness Regression Variables** & \multicolumn{2}{c}{**Coefficients**} & \multicolumn{2}{c}{**Coefficients**} & \multicolumn{1}{c}{**Coefficients**} & \multicolumn{1}{c}{\(T\)} \\ \cline{2-5}  & \(B\) & **Standard Error** & \(\beta\) & \\ \hline (A) Constant & 21.259 & 0.115 & & 184.892 \\ (1) Screen Size 1 & 1.905 & 0.145 & 0.399 & 13.180 \\ (2) Screen Size 2 & 0.707 & 0.145 & 0.147 & 4.890 \\ (B) Shooting Scene Brightness & 0.690 & 0.020 & 0.153 & 35.235 \\ (C) 2D or 3D Mode & \(-\)5.749 & 0.119 & \(-\)1.270 & \(-\)48.340 \\ (D) Photographic Equipment EV & \(-\)1.342 & 0.008 & \(-\)1.037 & \(-\)169.750 \\ (E) Interaction Value of Variables & & & & \\ (1) and (C) & \(-\)0.314 & 0.030 & \(-\)0.051 & \(-\)10.499 \\ (1) and (D) & \(-\)0.096 & 0.010 & \(-\)0.293 & \(-\)9.753 \\ (2) and (D) & \(-\)0.045 & 0.010 & \(-\)0.137 & \(-\)4.549 \\ (B) and (C) & \(-\)0.137 & 0.0(4) Photographic equipment \(EV\)  
In the experiment with different aperture and shutter conditions, this setting has a direct impact upon the image brightness. A larger \(EV\) indicates less exposure, and the statistical results are also consistent with this finding. (5) Interaction values of variables  
Each of the variables interacts with the others to a greater or lesser degree, but the coefficients of a given variable have a lesser effect on the screen brightness than interactions with all other major independent variables. Figure 9 is a standardized residuals histogram that shows the regression distribution of the standardized residuals. Figure 10 is a normal probability plot diagram (P-P diagram).