# 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 & 24.58 & 26.10 & 25.27 & 24.36 & 26.13 & 25.95 & 24.60 & 26.24 & **26.54** \\  & SSIM & 0.8381 & 0.8678 & 0.8477 & 0.8472 & 0.861 & 0.8596 & 0.8573 & 0.8676 & **0.8703** \\ \hline \multirow{2}{*}{**Barbara**} & PSNR & 24.31 & 28.18 & 26.60 & 26.67 & 27.21 & 27.29 & 28.56 & 29.18 & **29.75** \\  & SSIM & 0.8417 & 0.9149 & 0.8801 & 0.8877 & 0.8935 & 0.8926 & 0.9254 & 0.9264 & **0.9311** \\ \hline \multirow{2}{*}{**Boat**} & PSNR & 26.50 & 27.53 & 27.29 & 26.07 & 27.10 & 27.12 & 26.44 & 27.49 & **27.65** \\  & SSIM & 0.8723 & **0.8964** & 0.8893 & 0.8815 & 0.7781 & 0.8913 & 0.8893 & 0.8938 & 0.8958 \\ \hline \multirow{2}{*}{**Butterfly**} & PSNR & 20.56 & 25.10 & 21.91 & 23.07 & 23.72 & 24.19 & 24.21 & 24.46 & **26.34** \\  & SSIM & 0.8599 & 0.9251 & 0.8755 & 0.9221 & 0.9090 & 0.9280 & 0.9426 & 0.9440 & **0.9474** \\ \hline \multirow{2}{*}{**Columbia**} & PSNR & 23.76 & 26.46 & 25.07 & 25.84 & 25.45 & 27.46 & 26.36 & 26.94 & **28.02** \\  & SSIM & 0.8859 & 0.9338 & 0.9229 & 0.9272 & 0.9160 & 0.9288 & 0.9404 & 0.9395 & **0.9429** \\ \hline \multirow{2}{*}{**Cornfield**} & PSNR & 26.69 & 28.97 & 28.88 & 26.62 & 28.50 & 28.97 & 28.36 & 29.19 & **30.42** \\  & SSIM & 0.8954 & 0.9318 & 0.9269 & 0.9201 & 0.9027 & 0.9303 & 0.9326 & 0.9371 & **0.9422** \\ \hline \multirow{2}{*}{**Couple**} & PSNR & 25.08 & 27.87 & 27.44 & 26.10 & 27.42 & 27.56 & 27.13 & 27.84 & **28.04** \\  & SSIM & 0.8590 & 0.9071 & 0.8932 & 0.8841 & 0.8915 & 0.8980 & 0.9010 & 0.9044 & **0.9073** \\ \hline \multirow{2}{*}{**Goldhill**} & PSNR & 26.40 & 28.50 & 28.24 & 25.06 & 27.95 & 28.38 & 28.21 & 28.78 & **28.82** \\  & SSIM & 0.8612 & 0.8975 & 0.8877 & 0.8624 & 0.8856 & 0.8909 & 0.8948 & 0.8978 & **0.8997** \\ \hline \multirow{2}{*}{**Hat**} & PSNR & 27.28 & 31.63 & 28.72 & 29.74 & 30.35 & 31.04 & 31.20 & 31.82 & **32.56** \\  & SSIM & 0.8994 & 0.9501 & 0.9315 & 0.9431 & 0.9442 & 0.9427 & 0.9522 & 0.9576 & **0.9636** \\ \hline \multirow{2}{*}{**Man**} & PSNR & 23.14 & **26.35** & 24.83 & 24.30 & 25.46 & 25.72 & 24.06 & 25.68 & 25.54 \\  & SSIM & 0.8350 & **0.8843** & 0.8613 & 0.847 & 0.8748 & 0.8727 & 0.8758 & 0.8845 & 0.8799 \\ \hline \multirow{2}{*}{**Peppers**} & PSNR & 24.29 & 29.93 & 27.37 & 27.92 & 28.46 & 28.85 & 29.08 & 29.50 & **29.98** \\  & SSIM & 0.8584 & 0.9293 & 0.9062 & 0.9098 & 0.9229 & 0.9164 & 0.9294 & 0.9300 & **0.9306** \\ \hline \multirow{2}{*}{**Cameraman**} & PSNR & 23.89 & 27.55 & 26.29 & 26.96 & 26.84 & 26.83 & 26.31 & 27.37 & **27.88** \\  & SSIM & 0.8858 & 0.9402 & 0.9290 & 0.9332 & 0.9298 & 0.9327 & 0.9464 & 0.9482 & **0.9484** \\ \hline \multirow{2}{*}{**Tower**} & PSNR & 23.68 & 26.78 & 26.13 & 25.36 & 25.17 & 26.53 & 25.71 & 26.50 & **27.15** \\  & SSIM & 0.8801 & 0.9197 & 0.9089 & 0.9093 & 0.9080 & 0.9175 & 0.9191 & 0.9262 & **0.9279** \\ \hline \multirow{2}{*}{**Average**} & PSNR & 24.63 & 27.77 & 26.46 & 26.01 & 26.90 & 27.38 & 26.94 & 27.77 & **28.36** \\  & SSIM & 0.8671 & 0.9152 & 0.8969 & 0.8981 & 0.8936 & 0.9078 & 0.9159 & 0.9198 & **0.9221** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index in comparison to EC algorithms with \(16\times 16\) regular isolate block losses. was over 0.52 dB in terms of PSNR and 0.0034 in terms of SSIM. Similarly, compared with the POCS, VC, SLP, and MRF methods, our method obtained gains of 3.16 dB, 1.84 dB, 1.59 dB, and 1.1 dB in terms of PSNR and gains of 0.0967, 0.0674, 0.0129, and 0.0261 in terms of SSIM, respectively. Moreover, compared with the recent image EC method KMMSE, the average PSNR gain is 0.67 dB. Finally, we compared the reconstruction quality of our method with the other methods on random consecutive block losses.