# Image Error Concealment Based on Deep Neural Network
## 4 Experiments
### Objective and Subjective Performance Comparison
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Images**} & \multirow{2}{*}{**Metrics**} & \multicolumn{8}{c}{**Methods**} \\ \cline{3-11}  & & **POCS** & **OAI** & **AVC** & **CAD** & **MRF** & **VC** & **SLP** & **KMMSE** & **Ours** \\ \hline \multirow{2}{*}{**Baboon**} & PSNR & 22.93 & 23.72 & 23.81 & 20.44 & 24.38 & 18.93 & 22.76 & 24.20 & **24.47** \\  & SSIM & 0.7852 & 0.8142 & 0.7986 & 0.7801 & 0.8125 & 0.7837 & 0.8049 & 0.8146 & **0.8163** \\ \hline \multirow{2}{*}{**Barbara**} & PSNR & 22.92 & 25.80 & 25.07 & 21.62 & 25.85 & 19.91 & 26.76 & 27.77 & **27.77** \\  & SSIM & 0.7995 & 0.8747 & 0.8445 & 0.8327 & 0.8621 & 0.8235 & 0.8959 & 0.8997 & **0.9033** \\ \hline \multirow{2}{*}{**Boat**} & PSNR & 25.37 & 26.08 & 26.44 & 21.85 & 26.38 & 18.01 & 25.11 & 26.17 & **26.64** \\  & SSIM & 0.8336 & 0.8611 & 0.8601 & 0.825 & 0.8590 & 0.8089 & 0.8564 & 0.8635 & **0.8637** \\ \hline \multirow{2}{*}{**Butterfly**} & PSNR & 19.27 & 21.96 & 19.93 & 18.49 & 21.58 & 17.93 & 21.54 & 22.05 & **23.03** \\  & SSIM & 0.8031 & 0.8754 & 0.827 & 0.8484 & 0.8739 & 0.8422 & 0.9038 & 0.9008 & **0.9111** \\ \hline \multirow{2}{*}{**Columbia**} & PSNR & 22.26 & 23.77 & 22.86 & 22.59 & 23.10 & 21.80 & 23.79 & 24.25 & **24.54** \\  & SSIM & 0.8463 & 0.8898 & 0.8875 & 0.8806 & 0.8757 & 0.8537 & 0.8990 & 0.9063 & **0.9077** \\ \hline \multirow{2}{*}{**Cornfield**} & PSNR & 25.23 & 25.04 & 26.33 & 21.32 & 26.05 & 19.42 & 25.51 & **26.48** & 25.87 \\  & SSIM & 0.8555 & 0.8869 & 0.8948 & 0.8591 & 0.8785 & 0.8263 & 0.8930 & **0.9004** & 0.8963 \\ \hline \multirow{2}{*}{**Couple**} & PSNR & 23.60 & 25.45 & 25.66 & 22.06 & 25.42 & 19.77 & 24.15 & 25.31 & **26.40** \\  & SSIM & 0.8125 & 0.8571 & 0.8544 & 0.8281 & 0.8505 & 0.8214 & 0.8520 & 0.8574 & **0.8656** \\ \hline \multirow{2}{*}{**Goldhill**} & PSNR & 25.16 & 26.61 & 27.13 & 22.70 & 27.12 & 20.81 & 26.76 & **27.51** & 27.31 \\  & SSIM & 0.8177 & 0.8546 & 0.8498 & 0.8069 & 0.8460 & 0.8102 & 0.8574 & **0.8608** & 0.8552 \\ \hline \multirow{2}{*}{**Hat**} & PSNR & 26.18 & 27.83 & 26.98 & 21.36 & 28.42 & 19.5802 & 27.50 & 29.15 & **29.28** \\  & SSIM & 0.8763 & 0.9076 & 0.9081 & 0.8661 & 0.9278 & 0.8344 & 0.9294 & **0.9386** & 0.9346 \\ \hline \multirow{2}{*}{**Man**} & PSNR & 21.39 & 23.17 & 22.36 & 20.49 & 22.77 & 21.2781 & 21.71 & 22.76 & **23.48** \\  & SSIM & 0.7783 & 0.8308 & 0.8125 & 0.7793 & 0.8249 & 0.8096 & 0.8211 & 0.8315 & **0.8346** \\ \hline \multirow{2}{*}{**Peppers**} & PSNR & 21.66 & 25.41 & 23.28 & 21.35 & 24.05 & 20.09 & 24.75 & 25.06 & **25.67** \\  & SSIM & 0.8163 & 0.8772 & 0.8542 & 0.836 & 0.8733 & 0.8284 & 0.8892 & 0.8925 & **0.8955** \\ \hline \multirow{2}{*}{**Cameraman**} & PSNR & 22.71 & 25.68 & 24.95 & 21.51 & 25.77 & 19.27 & 25.36 & 25.79 & **26.61** \\  & SSIM & 0.8452 & 0.8971 & 0.8952 & 0.8796 & 0.8974 & 0.8361 & 0.9128 & 0.9142 & **0.9164** \\ \hline \multirow{2}{*}{**Tower**} & PSNR & 22.22 & 23.99 & 24.35 & 21.81 & 23.44 & 19.84 & 24.85 & **25.44** & 25.01 \\  & SSIM & 0.8276 & 0.8704 & 0.869 & 0.854 & 0.8626 & 0.8321 & 0.8854 & **0.8918** & 0.8834 \\ \hline \multirow{2}{*}{**Average**} & PSNR & 23.15 & 24.96 & 24.55 & 21.35 & 24.95 & 19.74 & 24.66 & **25.53** & **25.85** \\  & SSIM & 0.8229 & 0.8690 & 0.8581 & 0.8366 & 0.8649 & 0.8239 & 0.8769 & 0.8825 & **0.8834** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PSNR and SSIM comparison of EC algorithms with \(16\times 16\) random consecutive block losses. Figure 21: Subjective comparison of different EC algorithms on the Cameraman image with \(16\times 16\) random block loss. The corresponding PSNR and SSIM values are also shown in the upper left corner. Figure 20: Subjective comparison of different EC algorithms on the Butterfly image with \(16\times 16\) consecutive block loss. The corresponding PSNR and SSIM values are also shown in the upper left corner. Regarding the run time of our proposal, since we needed to train an AE-P network for each corrupted image, the training time of the network was included in the entire image processing time.