# Parallel Image Completion with Edge and Color Map
## 4 Experimental Results and Analysis
### Quantitative Comparison  
In addition to the visual results, the quantitative performance of the proposed PIC-EC method is also evaluated using the following four metrics: (1) relative \(\ell_{1}\); (2) peak signal-to-noise ratio (PSNR); (3) structural similarity index (SSIM) with a window size of 11 and (4) Frechet Inception Distance (FID) [50]. PSNR directly measures the difference in pixel values and SSIM estimates the holistic similarity between two images. They assume that the ideal recovered results are exactly the same as the target images. They are the most used evaluation criteria among the image generation community. However, these two metrics assume pixel-wise independence, which may assign favorable scores to perceptually inaccurate results. Therefore, the FID metric is also used to evaluate the output quality because it is closer to human perception. FID measures the Wasserstein-2 distance between the feature space representations of real and completed images using the pretrained Inception-V3 model. The experiment is conducted over CelebA-HQ and Paris StreetView test datasets for six different squared masks include \(70\times 70\), \(80\times 80\), \(90\times 90\), \(100\times 100\), \(110\times 110\) and \(128\times 128\). The location of  
Figure 7: Comparison of qualitative results with the existing methods on random regular masked Paris StreetView. (**a**) ground truth images; (**b**) masked input images; (**c**) results of the FMM; (**d**) results of the GMCNN; (**e**) results of the EdgeConnect; (**f**) results of PIC-EC, best viewed with zoom-in. each mask is random. We run each method on the corrupted images and get the final completed results. The statistics results are based on the completed image, which are mostly comprised of the ground truth images and are reported in Tables 3 and 4, respectively. Figures 8 and 9 show these statistics results in an intuitive way. We find that as the mask size increases, the performance of all the methods has declined gradually to varying degrees, which is in line with our intuitive experience. The methods will get less information to fill the missing region as the masks get larger. Obviously, the FMM method has the worst performance because it has no ability to generate semantic contents, especially in the larger mask cases. Overall, the EdgeConnect and PIC-EC are superior to other methods because both of them only consider making the textures of the completed images realistic, but ignore the structure and color information of the images. When the mask size is small, the performance of EdgeConnect is better than that of PIC-EC because the effect of the color map is not obvious in these cases. However, the PIC-EC method performs better than EdgeConnect in all four metrics when the mask size becomes larger, such as \(110\times 110\) and \(128\times 128\). In particular, PIC-EC outperforms EdgeConnect by 0.28% in mean \(\ell_{1}\) error, 2.273 dB in PSNR, 0.013 in SSIM and 2.384 in FID over CelebA-HQ in the largest mask size. At the same time, for Paris StreetView, PIC-EC outperforms EdgeConnect by 0.5% in mean \(\ell_{1}\) error, 1.4 dB in PSNR, 0.039 in SSIM and 0.377 in FID. Both PIC-EC and EdgeConnect achieve small \(\ell_{1}\) error; however, the \(\ell_{1}\) metric simply favors smooth and blurry results. PIC-EC has a better FID performance than EdgeConnect, which indicates that the images completed by PIC-EC are more plausible since the FID metric is closer to human perception. This should be accredited to enough prior information learned by PIC-EC. Therefore, the PIC-EC method achieves quantitative results exceeding others by a considerable margin under all the four metrics and can generate more realistic images with the guidance of edge and color maps. Other methods only consider making the textures of the completed image realistic, but ignore the structures and colors of the image. Furthermore, PIC-EC with edge guidance brings consistent improvements over others.