## 5 Experiments
### Experimental Settings (Parameters)
We trained the Pix2 pix network with the same ground truth images used in our Seg2 pix training, and the results are visualized in Figure 9. Compared with Pix2 pix, we may also observe obvious differences in the color distribution and borderlines with less color bleeding in the qualitative comparison. ### Analysis of the Results

As shown in Figures 9-11, the raw generated image from the original image shows the correct color distribution and shading, as we expected to obtain with only a few training data. All these outputs were accomplished with one Seg2 pix network. Generating segmented image data and colorized output was performed gradually by the two-step training. \begin{table}
\begin{tabular}{l l} \hline \hline
**Training Configuration** & **LPIPS (Average)** \\ \hline Style2 paints & 0.3372 \\ PaintsChainer & 0.1772 \\ Tag2 pix & 0.0723 \\ Seg2 pix (without trap ball and attention) & 0.0488 \\ Seg2 pix (without trap ball) & 0.0478 \\ pix2 pix & 0.0477 \\ Seg2 pix (without attention layer) & 0.0411 \\
**Seg2 pix (ours)** & **0.0375** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the Learned Perceptual Image Patch Similarity (LPIPS) in our experiments showing a better score compared to the other colorization models, even against Pix2 pix, which was trained on the same dataset as Seg2 pix. Figure 9: The Pix2 pix generated images (1) from Dataset B and (2) from Dataset D show only slight differences in comparison to the Seg2 pix output. However, the other Seg2 pix generated images show very clear differences in the color bleeding and quality compared to the Pix2 pix generated image trained on the same ground truth. Specifically, for the Pix2 pix result (2) from Dataset B and (1) from Dataset C, these show a very low-quality color distribution between the skin and clothes. Figure 10: Comparison of the line art colorization results. The first column shows the original ground truth image. Columns 2–5 show the automatic colorization results; from left to right: Seg2 pix, PaintsChainer (Canna) [6], Tag2 pix [16], and Style2 points [7]. Rows 1–3 of the Seg2 pix column are the results from the model trained on Dataset A ([PERSON]’s cell series), and Rows 4–6 are the results from the model trained on Dataset B (Fantasy sister series). ## 6 Conclusions

In this paper, we propose an automatic colorization network, which produces high-quality line art colorization with only a few training images. We applied data preprocessing steps before training to achieve high-quality segmentation images from the generator. Figure 11: Comparison of the line art colorization results. The first column shows the original ground truth image. Columns 2–5 show the automatic colorization results; from left to right: Seg2 pix, PaintsChainer (Canna) [6], Tag2 pix [16], and Style2 Points [7]. Rows 1–3 of the Seg2 pix column are the results from the model trained on Dataset C (Fantasy sister series), and Rows 4–6 are the results from the model trained on Dataset D (Fantasy sister series). The generated segmented image becomes the suggestion map for auto-colorization and generating the step runs again for coloring, which is the step that is the most different from the Pix2 pix method. We showed its performance through experiments with sets of character image data obtained from well-known webtoon series. The experimental results showed the potential of the method to reduce the amount of iterative work in practice and supporting artists to focus on the more creative aspects of the webtoon creation pipeline. As an idea for real use scenarios, we can colorize 100 sketch images of the same character by coloring and labeling only 10 or up to 20 sketch images with this Seg2 pix method. Especially in the webtoon and animation industries, coloring over thousands of the same character's sketch images is a repetitive job, so the contribution of this method is very suitable in these industries.