## 3 Experimental Results
### Results and Analysis  
We evaluated the performance of the hologram-encoding methods based on the aforementioned three image quality measurement metrics, average holographic 3D image's brightness (HIB), holographic contrast ratio (HCR), and holographic uniformity of surface (HUS). For the performance evaluation, we set each rectangular area sample (unit area's size: 27 \(\times\) 38 pixels) of a bright state area and dark state area in the reconstructed H3D scene as the scanning region (see Figure 9). Figure 11 shows a typical surface intensity map in the bright state area of a reconstructed H3D scene according to each encoding method. Figure 12 shows the box-whisker plot that maps the surface intensity of bright/dark area samples to be scanned on each H3D scene optically reconstructed from four encoding types. From both the holographic surface uniformity (HUS) characteristic and the holographic intensity (HIB) measurement in the bright areas, we first found that the surface uniformity of H3D reconstruction is highest in the order of MAO, LEE/BUR, and CAO. Moreover, from the HIB test using samples of bright areas, we found that although the average brightness  
Figure 12: Intensity measurement of (**a**) bright area and of (**b**) dark area in H3D images optically reconstructed using four kinds of encodings. of MAO is about 10.4% lower than that of CAO, MAO's average brightness is about 11.6% higher than that of BUR or LEE. Second, Figure 13 presents a HCR-HIB diagram of the optically reconstructed H3D scenes with regard to the four encoding methods. The average brightness of [PERSON]'s and [PERSON]'s encoding methods is approximately 19.6% lower than that of CAO encoding. However, the HCR values of these two encodings are at least two times higher than that of the CAO encoding. Above all, the diagram shows that the HCR of the MAO method is three times greater than that of the CAO method at the cost of only a 10% decrease in brightness in comparison with the brightness of the CAO encoding. Furthermore, the HCR of the MAO method is still 30% higher than that of [PERSON]'s encoding or [PERSON]'s encoding. Thus, the analysis through the HCR-HIB diagram and the brightness graph in Figure 12a indicates that the MAO approach proposed in this study not only provides the most effective eliminator of DC background noise but also achieves refined performance in holographic contrast ratio (HCR) in comparison to [PERSON]'s and [PERSON]'s methods. Third, we also assessed the surface uniformity of the reconstructed H3D object in brightness distribution by the method of a box-whisker plot, where a parameter of IQR (Inter Quartile Range) is defined as the range of 50% data centered on a median value, equal to each box's length at the boxplot; the smaller the IQR value is, the more uniform the surface of a holographic reconstructed object is. Through observation of each sample in the local bright area, we found that CAO's IQR is 20.5, MAO's IQR is 14, [PERSON]'s IQR is 9, and [PERSON]'s IQR is 11. The results are derived from consideration of only 50% of the surface intensity data, and thus [PERSON]'s encoding has the smallest IQR. In addition, we checked the characteristic behavior of outliers in the box-whisker plot; in fact, all encoding methods except for the MAO encoding case have outlier values, where data of brightness are positioned out of the boxplot's whisker (see Figure 12a). CAO encoding has 20 outliers, [PERSON]'s encoding has 23 outliers, and [PERSON]'s encoding has 14 outliers, whereas MAO encoding has no outlier. Here, it is necessary to note that these outliers correlate with the extent of intensity irregularity in the measured local surface. A wider fluctuation of the intensity distribution means that the chosen surface in the given holographic 3D scene has less uniformity.