# Image Error Concealment Based on Deep Neural Network
## 4 Experiments
### Comparative Studies  
In our method, in order to reduce the prediction error and improve the accuracy of the AE-P network, we corrected the predictions based on the non-local self-similarity of the natural images. Moreover, we proposed an adaptive scan order based on the joint credibility of the support area and reconstruction to alleviate the error propagation problem. To evaluate the influence of the proposed correction and scan order to the final EC performance, we conducted three group comparative experiments corresponding to the three loss modes on the test images. In every group, there were three different scenarios of our method: a scenario that did not use the proposed correction, a scenario that did not use the proposed adaptive scan order, and a scenario that utilized both the proposed correction and the adaptive scan order. For convenience, we named these three scenarios 'Cor(off)-Ord(on)', 'Cor(on)-Ord(off)', and 'Cor(on)-Ord(on)'. 'Cor' and 'Ord' correspond to the proposed correction and scan order, while 'on' and 'off' in the brackets represent whether they were used or not used. The results of the comparative experiments are shown in Figures 13-15. As can be seen from Figures 13-15, in all three loss modes, scenario 'Cor(on)-Ord(on)' performed best in almost all of the test images in terms of PSNR and SSIM. In particular, scenario 'Cor(on)-Ord(on)' outperformed scenario 'Cor(off)-Ord(on)' by a large margin under all the loss modes and test images. The great improvement of reconstruction performance is due to the proposed correction, which can reduce the prediction error effectively. Moreover, it could be observed that the proposed adaptive scan order could also improve the reconstruction performance. Compared with the proposed correction, \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multicolumn{2}{c}{**Number of Neurons**} \\ \cline{2-3}  & **AE** & **Prediction** \\ \hline FC 1 & & 49 \\ FC 2 & & 45 \\ FC 3 & & 40 \\ FC 4 & & 35 \\ FC 5 & & 30 \\ FC 6 & & 25 \\ FC 7 & 30 & 20 \\ FC 8 & 35 & 15 \\ FC 9 & 40 & 10 \\ FC 10 & 45 & 5 \\ FC 11 & 49 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Architectures of the designed AE-P network. After each layer except for the last one we used the ‘elu’ activation function, and after the last layer, we used the ‘tanh’ activation function. there is less performance improvement with the scan order. This is because the scan order obtains a better reconstruction performance of the structure, which sometimes produces false edges and leads to the reduction of the PSNR. For example, the scan order fails to improve the reconstruction performance under PSNR in Hat with random loss mode, as shown in Figure 15.  
Figure 14: The influence of the proposed correction and adaptive scan order to the final EC performance under the \(16\times 16\) regular consecutive loss mode. Figure 13: The influence of the proposed correction and adaptive scan order to the final error concealment (EC) performance under the \(16\times 16\) regular isolated loss mode. Figure 15: The influence of the proposed correction and adaptive scan order to the final EC performance under the \(16\times 16\) random loss mode. For further evaluation of the influence of the proposed correction and adaptive scan order to the final EC performance, subjective comparisons are given in Figures 16-18 corresponding to the three different loss modes. It can be observed that scenario 'Cor(on)-Ord(on)' achieved the most comfortable visual and the highest PSNR and SSIM. Specifically, through comparing the method of scenario 'Cor(off)-Ord(on)' and the method of scenario 'Cor(on)-Ord(on)', we can observe that the proposed correction can greatly improve the reconstruction performance. As shown in the red box in Figure 16c, the bracket inside the red box is disconnected, while the bracket in Figure 16e is connected.