## 4 Result and Evaluation  
In order to verify the effectiveness of the proposed method, we conducted an experiment to compare our results with the existing method. Both [12; 13] have the same purpose as ours, that is, achieving color compensation by displaying overlay image on OST-HMD. Both methods do not support the light subtraction. We choose to compare with [13] as it also uses daltonization so that we can confirm the effect of the light subtraction while eliminating other factors caused by the difference of compensation methods. Given the \(I_{S}\) captured by the scene camera from the monitor, the \(I_{T}\) is equal to the target image for deuteranopia, which is obtained by adding a compensation image computed with Equation (1) to the \(I_{S}\). Denoting the image provided to the user with our prototype system as \(I_{UI}^{our}\) and with the existing method [13] as \(I_{UI}^{ex}\), we compared the similarities of \(I_{UI}^{our}\) and \(I_{UI}^{ex}\) to the computed target image \(I_{T}\). \(I_{UI}^{our}\), which is captured with the user-perspective camera, is generated by first computing the compensation image with Equation (1) using \(I_{S}\) and \(I_{T}\), and then by computing the LCD panel controlling image \(I_{L}\) and the OST-HMD overlay image \(I_{H}\) with the estimated \(f_{subtraction}\), \(f_{decay}\), and \(f_{distortion}\) using Equation (2). \(I_{UI}^{ex}\), which is also captured by a user perspective camera, is generated by displaying the compensation image generated with Equation (1) directly on the OST-HMD with the LCD removed from the system. For those pixels with negative values in the compensation image, we changed the negative value to 0. With the LCD installed, the color area that can be observed by the user is actually smaller than the area without the LCD because of the decay and the material color of the LCD. As shown in Figure 10, with the compensation for both the OST-HMD and the LCD, our method (Figure 10, 3 rd column) appeared to be more similar to the target images (Figure 10, 2 nd column) and showed enhanced contrast as compared to the existing method (Figure 10, 4 th column) for the areas where contrast loss tends to occur with deuteranopia. The resolution of all images is \(400\times 300\), and the average processing time was 30.91 milliseconds. Furthermore, as a quantitative evaluation, we used the root mean square (RMS) error metric proposed in [24] to compare our method with the existing method. The RMS evaluates the consistency of the local contrast between a pixel \(q_{i}\) and its randomly picked neighboring pixels (\(k\) in total) \(S_{qi}=\{q_{i},q_{i+1},...,q_{i+k-1}\}\) in the test image with that of the corresponding pixel \(p_{i}\) and the neighboring pixels \(S_{pi}=\{p_{i},p_{i+1},...,p_{i+k-1}\}\) in the reference image,  
\[RMS(q_{i})=\sqrt{\frac{1}{k}\sum_{s=j}^{j+k-1}{(\frac{(p_{i}-p_{s})-(q_{i}-q_{ s})}{160})^{2}}},\]  
where the constant 160 was introduced to normalize the index value to the range of [0,1]. We applied the RMS to the CVD simulation image of the target image, the simulation result using the proposed method, and the simulation result using the existing method. The simulation result of the original image was used as a reference image. By contrasting the results of the proposed method to the results of the existing method (shown in Table 1), it is clear that the proposed method's results are superior other than for group C. Since the image of group C consists of more high frequency on the background, the contrast loss caused by such blurring artifacts, which will be discussed in the next section, becomes more dominant even though the foreground object that is supposed to be barely visible with deuteranopia becomes much clearer than in the existing method.