# Parallel Image Completion with Edge and Color Map
## 4 Experimental Results and Analysis
### User Study  
We perform a user study using the human perceptual metrics _Two Alternative Forced Choice_ (2 AFC) and _Just Noticeable Differences_ (JND) over CelebA-HQ and Paris StreetView datasets. 2 AFC is a method for measuring the subjective experience of a person through their pattern of choices and response times. JND is the amount something must be changed in order for a difference to be noticeable, detectable at least half the test time. In the 2 AFC test, the users are asked whether or not a randomly selected completed image from each method is real. For JND, the users are asked to select the more realistic image from pairs of real and completed images. Users are given two seconds to decide for each image. This experiment is performed over 300 images for each method and mask size. The results are presented in Tables 6 and 7. These results show that there are more images generated by PIC-EC being favored by the users because PIC-EC generates sharper images with more details.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Mask Size** & **GLCIC** & **DeepFill** & **GMCNN** & **DFNet** & **EdgeConnect** & **PIC-EC (Ours)** \\ \hline \multirow{4}{*}{2 AFC (\%) \({}^{*}\)} & 70 & 37.6 & 67.5 & 70.3 & 76.5 & **87.0** & 85.2 \\  & 80 & 26.7 & 63.3 & 57.3 & 65.4 & 76.7 & **83.3** \\  & 90 & 18.7 & 50.7 & 44.7 & 46.6 & 71.3 & **78.3** \\  & 100 & 12.7 & 37.3 & 39.3 & 38.7 & 61.5 & **70.3** \\  & 110 & 7.3 & 18.3 & 31.6 & 22.3 & 50.2 & **66.5** \\  & 128 & 3.3 & 7.3 & 20.3 & 17.7 & 40.3 & **61.7** \\ \hline \multirow{4}{*}{JND (\%) \({}^{*}\)} & 70 & 16.7 & 21.2 & **44.0** & 33.3 & 36.7 & 40.5 \\  & 80 & 10.0 & 12.5 & 35.7 & 26.7 & 26.7 & **36.7** \\ \cline{1-1}  & 90 & 6.7 & 10.5 & 19.2 & 23.3 & 22.5 & **32.4** \\ \cline{1-1}  & 100 & 4.4 & 9.1 & 15.2 & 16.7 & 18.4 & **28.6** \\ \cline{1-1}  & 110 & 2.4 & 5.3 & 6.7 & 13.3 & 17.6 & **26.3** \\ \cline{1-1}  & 128 & 2.0 & 2.6 & 5.1 & 10.0 & 12.5 & **21.2** \\ \hline \hline \end{tabular}
\end{table}
Table 6: User study results over the CelebA-HQ for six different squared masks with methods: GLCIC, DeepFill, GMCNN, DFNet, EdgeConnect and PIC-EC. Boldface in each row indicates the best performance. \({}^{*}\) Higher is better.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mask Size** & **GMCNN** & **EdgeConnect** & **PIC-EC (Ours)** \\ \hline \multirow{4}{*}{2 AFC (\%) \({}^{*}\)} & 70 & 73.3 & 70.5 & **86.7** \\  & 80 & 68.3 & 67.7 & **83.1** \\  & 90 & 66.7 & 53.3 & **75.5** \\  & 100 & 57.4 & 40.4 & **65.0** \\  & 110 & 46.5 & 36.7 & **60.3** \\  & 128 & 40.2 & 28.2 & **57.2** \\ \hline \multirow{4}{*}{JND (\%) \({}^{*}\)} & 70 & 38.7 & 41.9 & **45.5** \\  & 80 & 32.2 & 35.3 & **37.5** \\ \cline{1-1}  & 90 & 29.1 & 32.5 & **34.4** \\ \cline{1-1}  & 100 & 22.6 & 28.1 & **29.1** \\ \cline{1-1}  & 110 & 16.1 & 24.2 & **26.5** \\ \cline{1-1}  & 128 & 12.9 & 23.3 & **24.2** \\ \hline \hline \end{tabular}
\end{table}
Table 7: User study results over the Paris StreetView for six different squared masks with methods: GMCNN, EdgeConnect and PIC-EC. Boldface in each row indicates the best performance. \({}^{*}\) Higher is better.