# Internet Video Delivery Improved by Super-Resolution with GAN
## 7 Experimental Results
### Results of Video Quality Assessment  
First, we tested the trained models using the VideoSet 360p and 540p samples with lossless compression (QP = 0). Then, according to the SR model training setup, we upscaled the samples by a factor of 2. Finally, we used the PSNR, LPIPS, and VMAF to assess and compare the quality of SR and the ground truth videos. Figure 3 shows the average values measured using PSNR, LPIPS, and VMAF with a confidence interval of 95%. High values of PSNR and VMAF indicate that the quality of the video can engage the audience. A high LPIPS value means that the assessed video quality can disrupt the audience. The assessment of video quality using the PSNR metric (see Table 4) showed that the SRResNet and VSRGAN+ models had the best outcomes of 38.44 dB (\(\pm 0.47\)) and 38.09 dB (\(\pm 0.49\)) in super-resolved 720p video resolution, and 39.65 dB (\(\pm 0.37\)) and 39.34 dB (\(\pm 0.39\)) in super-resolved 1080p video resolution, respectively. In the same scenario, the worst outcome was recorded by the SRGAN model, with 33.69 dB (\(\pm 0.56\)) and 34.14 dB (\(\pm 0.56\)), respectively. The SRCNN and ESPCN models showed middle-ground  
Figure 3: Restoration video quality assessment using PSNR, LPIPS, and VMAF. (**a**) The PSNR quality assessment. (**b**) The LPIPS quality assessment. (**c**) The VMAF quality assessment. outcomes of 35.89 dB (\(\pm\)0.51) and 35.68 dB (\(\pm\)0.50) in super resolving 720p video resolution, and 37.19 dB (\(\pm\)0.43) and 37.01 dB (\(\pm\)0.42) in super-resolved 1080p video resolution. The SRGAN's outcome is due to the perceptual nature of its loss function, with little attachment to the video artifacts measured by the PSNR metric. The assessment of video quality using the LPIPS metric showed that the VSRGAN+ and SRGAN models had the best outcomes, i.e., the shortest LPIPS distances, among the evaluated models. The VSRGAN+ super-resolved videos, at resolutions of both 720p and 1080p, had LPIPS values equal to 0.039 (\(\pm\)0.003) and 0.046 (\(\pm\)0.004). The SRGAN's LPIPS values were 0.044 (\(\pm\)0.003) and 0.050 (\(\pm\)0.004). The other three models ranked first among the longest LPIPS distances, with 0.057 (\(\pm\)0.007) and 0.063 (\(\pm\)0.007) as recorded by SRResNet; 0.064 (\(\pm\)0.004) and 0.066 (\(\pm\)0.005) as recorded by SRCNN; 0.064 (\(\pm\)0.004) and 0.066 (\(\pm\)0.005) as recorded by ESPCN. Both 720p and 1080p super-resolved video resolutions recorded these values. The assessments of video quality using the VMAF metric showed that the VSRGAN+ model had the best outcomes among the evaluated models. These assessments showed VMAF values of 96.62 (\(\pm\)0.55) and 97.08 (\(\pm\)0.47) for the 720p and 1080p video resolutions, respectively. The ESPCN model's VMAF values were 92.53 (\(\pm\)0.66) and 93.64 (\(\pm\)0.80) for 720p and 1080p video resolutions. The other three models, SRGAN, SRResNet, and SRCNN, presented the lowest VMAF values. Second, we applied VMAF to evaluate the perceptual quality of the videos by changing the bitrate through QP in \([0;51]\). We compare the quality of two-fold upscaled videos, i.e., 540p to 1080p, using VSRGAN+ and the other models in Table 1. We also compare them with compressed videos within the range of 0 to 51 alone. Figure 4 presents the assessed perceptual quality. In Figure 4, the quality considers the compression levels in bitrates; Figure 4 includes the compression levels according to the QP. The results show that the VSRGAN+ model outperformed the quality presented by the other models for compression levels up to 42 QP; only for compression levels greater than 42 QP did the CISRDCNN model outperform VSRGAN+. The training of models uses images of the Places365-Standard dataset [60], except for the model CISRDCNN, which uses images with compression artifacts, defined by the quality factor (QF) 20 JPEG, applied for the same dataset. Although the CISRDCNN model targets images with compression artifacts, it did not stand out in our experiments even when we used compressed videos.