# Automatic Colorization of Anime Style Illustrations Using a Two-Stage Generator
## 4 Experiments and Analysis
### Visual Analysis  
A good automatic colorization model should not only work on artificially generated line-art but also on real-world line-arts. To achieve high quality result on both images, we proposed to use histogram equalization and to take account of LDM in calculation of line loss (\(\mathcal{L}_{line}\)). Figure 8 presents visual comparison of different algorithms: Model proposed by [PERSON] et al. [10] (Base), Pix2 Pix [2], proposed model without histogram equalization, proposed model without line loss, and proposed model with histogram equalization and LDM applied line loss. The results show that the proposed model works with both types of images. Figure 8b shows checkerboard artifacts [24] from transpose convolution. Pix2 pix also shows that the result is very sensitive to type of input data and is very unstable.  
The effect of histogram equalization and \(\mathcal{L}_{line}\) is clearly shown in Figures 8a,b and 9a. Without histogram equalization, colors are not contained within the line contours but spilled or smeared over the boundaries of lines. When both methods are used in the colorization, that is Figure 8e, the result shows that contours are clearly distinguished and colors are contained within the boundaries. Visual inspection (Figure 9) of the results shows that generalization performance of the proposed model is better than existing works.  
We also tested colorization performance of other styles of image, i.e., grayscale image with various line thickness, texture, and shading. The result is illustrated in Figure 10. In the case of Base model, black was the dominant color and shows a lot of artifacts. Colors in the Pix2 Pix model are skewed and also show a lot of artifacts. In the case of proposed model, the model presents a wider color range. Reasons that Base and Pix2 Pix cannot properly color the image, unlike the proposed model, is because the distribution of thick lines are not trained and texture present in the image distorts the line distribution.  
Figure 11 shows the performance of models with 64 randomly chosen color hints leaked from the original color image. To show that our model solves the line overfitting problem, we performed the same experiment on real-world line-arts and lines extracted with XDoG method. Results of Base and Pix2 Pix in XDoG line-arts show that they have low accuracy in reflecting input color hints. In the case of Pix2 Pix, it shows the checkerboard effect and other artifacts. On the contrary, our model closely follows the original image where hints are given. For example, right collar on the first row of Figure 11e XDoG has colored blue correctly because the hint is given in that region. The left collar is colored with color of hair because the color hint in that region is not given. In the case of real-world line-arts, both Base and Pix2 Pix fail to contain the color within the lines. Although Base model tries to color the image with respect to given hints, the colors are spilled and does not follow the given hints. Pix2 Pix, on the other hand, consistently shows various artifacts. Consistent with the XDoG case, the colorization of real-world line-arts follows the provided hints. In this case, both of the collars in the first row of Figure 11e real-world are colored with blue which is the color of hints in that region. From Figure 11, we can observe that colorization performance of the proposed model