# Automatic Colorization of Anime Style Illustrations Using a Two-Stage Generator
## 5 Conclusions  
In this paper, we proposed a Line Detection Model (LDM) loss function and histogram equalization for generalization of line distribution. We also proposed a new performance metric called Colorization FID (C-FID) that can provide fair comparison of colorization performance. The experiment results show that the quality of generated image is sensitive to line distribution. The reason for poor quality of generated image is that line distribution of artificially generated line contours is different from real-world line-arts. We compared Base model and the proposed model in four different scenarios. We used XDoG generated line-art and real-world line-arts with and without color hints. The result shows that using histogram equalization and LDM enabled line loss exhibits the best result. The Base model with XDoG generated line-art with and without color hints exhibits C-FID score of 44.70 and 35.83, respectively, whereas the proposed model in the same scenario exhibits 39.77 and 32.16, respectively. C-FID score of real-world line-art with color hint is more dramatic. The Base model exhibits 87.95 and the proposed model exhibits 5.51. From the result, we can conclude that generalization of line distribution leads to better quality of colorized image.  
Conceptualization, Y.L.; data curation, Y.L.; funding acquisition, S.L.; methodology, Y.L.; project administration, S.L.; supervision, S.L.; validation, Y.L. and S.L.; visualization, Y.L.; writing--original draft, Y.L. and S.L.; writing--review and editing, S.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**W/O Color Hint** & \multicolumn{3}{c}{**XDoG Line-Arts**} & \multicolumn{2}{c}{**Real-World Line-Arts**} \\ \hline
**Model** & **FID** & **STD** & **FID** & **STD** \\ \hline Base [10] & 44.70 & 2.01 & 109.39 & 0.40 \\ Pix2 pix [2] & 41.14 & 1.51 & 117.24 & 0.38 \\ Ours (LDM enabled line loss only) & 43.87 & 1.91 & 99.40 & 0.32 \\ Ours (Histogram equalization Only) & 39.54 & 1.84 & 92.63 & 0.24 \\
**Ours** & **39.77** & **1.75** & **98.75** & **0.17** \\ \hline
**With Color Hint** & \multicolumn{3}{c}{**XDoG Line-Arts**} & \multicolumn{2}{c}{**Real-World Line-Arts**} \\ \hline
**Model** & **C-FID** & **STD** & **C-FID** & **STD** \\ \hline Base [10] & 35.83 & 1.81 & 87.95 & 0.36 \\ Pix2 pix [2] & 34.21 & 1.12 & 94.14 & 0.31 \\ Ours (LDM enabled line loss only) & 35.51 & 1.74 & 61.83 & 0.26 \\ Ours (Histogram equalization Only) & 33.29 & 1.51 & 60.45 & 0.24 \\
**Ours** & **32.16** & **1.57** & **57.51** & **0.23** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative comparison of Colorization FID score. The score measured using total of 145,408 XDoG line-arts and 530 pairs of line-arts and their colorized version of artwork. Lower Colorization FID score is better.  
Figure 13: Hint mask for Colorization FID score evaluation.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019 RIG1A1100455) and Education and Research Program of AI Convergence Engineering for Industrial Intelligence in Gyeongsangnam-do.  
**Conflicts of Interest:** The authors declare no conflict of interest.