# Reduction of Compression Artifacts Using a Densely Cascading Image Restoration Network
## 4 Experimental Results  
We used 800 images from DIV2K [52] as the training images. After they were converted into YUV color format, only Y components were encoded and decoded by the JPEG codec  
Figure 6: Verification of the number of DC blocks (DC) in terms of AR performance and complexity by using the Classic5 dataset. The circle size represents the number of parameters. The x and y-axis denote the total memory size and PSNR, respectively. Figure 7: Verification of the number of dense layers (L) per DC block (DC) in terms of AR performance and complexity by using the Classic5 dataset. The circle size represents the number of parameters. The x and y-axis denote the total memory size and PSNR, respectively. under three image quality factors (10, 20, and 30). Through this process, we collected 1,364,992 patches of a 40 \(\times\) 40 size from the original and reconstructed images. To evaluate the proposed method, we used Classic5 [24] (five images) and LIVE1 [53] (29 images) as the test datasets and Classic5 as the validation dataset. All experiments were performed on an Intel Xeon Gold 5120 (14 cores @ 2.20 GHz) with 177 GB RAM and two NVIDIA Tesla V100 GPUs under the experimental environment described in Table 5.  
In terms of the performance of image restoration, we compared the proposed DCRN with JPEG, ARCNN [30], DnCNN [33], DCSC [42], IDCN [43] and RDN [44]. In terms of the AR performance (i.e., PSNR and SSIM), the number of parameters and total memory size, the performance comparisons between the proposed and existing methods are depicted in Figure 8.  
Tables 6-8 enumerate the results of PSNR, SSIM, and PSNR-B, respectively, for each of the methods studied. As per the results in Table 7, it is evident that the proposed method is superior to the others in terms of SSIM. However, RDN [44] demonstrate higher PSNR values. While DCRN shows a better PSNR-B compared to that of DnCNN, it has comparable performance with DCSC in terms of PSNR-B using the Classic5 dataset. While the RDN was likely to improve AR performance by increasing the number of network parameters, the proposed method was focused to design the lightweight network with the small number of network parameters. Figure 8: Comparisons of the network performance and complexity between the proposed DCRN and existing methods for the LIVE1 dataset. The circle size represents the number of parameters. (**a**) The x and y-axis denote the total memory size and PSNR, respectively. (**b**) The x and y-axis denote the total memory size and SSIM, respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Environments** & **Options** \\ \hline Input size (\(F_{\text{Int}}\)) & \(40\times 40\times 1\) \\ Label size (\(F_{\text{Out}}\)) & \(40\times 40\times 1\) \\ CUDA version & 10.1 \\ Linux version & Ubuntu 16.04 \\ Deep learning frameworks & Pytorch 1.4.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental environments. Table 9 classifies the network complexity in terms of the number of network parameters and total memory size (MB). The proposed DCRN reduced the number of parameters to as low as 72%, 5% and 2% of those needed in DnCNN, IDCN and RDN, respectively. In addition, the total memory size was as low as 91%, 41%, 17% and 5% of that required for DnCNN, DCSC, IDCN and RDN, respectively. Since the same network parameters were repeated 40 times in DCSC, the total memory size was large even though the number of network parameters was smaller than that of the other methods. As shown in Figure 9, the inference speed of the proposed method is greater than that of all networks, except for ARCNN. Although the proposed method is slower than ARCNN, it is clearly better than ARCNN in terms of PSNR, SSIM, and PSNR-B, as per the results in Tables 6-8. Figure 10 shows examples of the visual results of DCRN and the other methods on the test datasets. Based on the results, we were able to confirm that DCRN can recover more accurate textures than other methods.