# Automatic Colorization of Anime Style Illustrations Using a Two-Stage Generator
## 4 Experiments and Analysis
### Quantitative Analysis  
In order to evaluate the performance of the proposed model, we used the same data set described in Section 4.1 and performed 178 k steps of training on open sourced automatic colorization model used in Ci et al. [10]. To quantitatively measure the difference of models, we used Frechet Inception Distance (FID) score [31]. FID score measures the similarity of two data sets, and FID is generally known to have high correlation with human judgment of visual quality of a given image. It is often used in evaluating the quality of image generated by GAN. Base image used in FID is generated by Inception model which is trained with ImageNet [27] data set. Target image used in FID is generated by some other model, in our case, two stage generator with histogram equalization and LDM. By applying base and target image on Inception model, we obtain two feature maps. Frechet distance of normalization of feature map of base and target image gives us the FID score. We used 140K artificial line-arts generated by XDoG and 530 pairs of line-arts and their colorized version of artwork. [PERSON] et al. [10] and [PERSON] et al. [12] proposed not to use hints while comparing the quality of generated images with FID score because it can not fairly compare the colorization performance of a model. However, FID score is not adequate in measuring colorization performance because it does not take account of color and location information present in the hint. Even if we exploit the hint in measuring FID score, we can not fairly compare the two results unless the location of hints are controlled. Thus, we need to have another performance metric to properly take account of the accurate representation of hint color, locality of color, and color containment within the boundaries of line contours. In this paper, we propose another performance metric, called Colorization FID (C-FID), specifically designed for comparing the colorization performance. Figure 13 shows the layout of the measurement mechanism. Although, C-FID and FID score are fundamentally the same, the only difference is the use of hint in colorization. C-FID uses segment-based hint pointing method, whereas FID does not use hints. The calculation of the score is the same. We divide an image into seven segments-based on the composition locality present in illustrations. First set of segments are placed in four corners. Let width be \(w\), then we set the dimension of the corner segment as \(\frac{1}{4}\cdot w\) by \(\frac{1}{4}\cdot w\). We call this region the corner. In between the two corners on the left and and right hand side of the image, there is rectangle segments with dimension of \(\frac{2}{4}\cdot w\) by \(\frac{1}{4}\cdot w\). This region is called the wing. The rest of the image is called the body. Each corner contain one pixel hint in arbitrary location within the corner. Each wing contains two one pixel hints and the body contains 20 one pixel hints within each wing and body, respectively. Using this constraints, different models can be given a fair comparison because each segment is given a specific number of hint colors within limited space. Then, we measure FID score over the generated images. We call this method of performance measurement as Colorization FID (C-FID). Table 2 shows the comparison of C-FID scores on different models. Just as FID, the lower the C-FID score is, the better. The result shows that by applying the proposed line loss and histogram equalization, the proposed method achieves lower C-FID than the Base model in all scenarios. There are two interesting observations in the result of C-FID. In the case of real-world line-arts, C-FID score is more than two times larger than that of XDoG line-arts. On the other hand, standard deviation is at least five times lower than that of XDoG line-arts. As we can see from Figure 3, real-world line-art does not contain background texture where as colored version of the line-art is full of texture.