# End-to-End Online Video Stitching and Stabilization Method Based on Unsupervised Deep Learning
## 3 Experimental Results and Analysis
### Quantitative Results and Analysis  
To comprehensively evaluate the proposed method, we conducted extensive comparisons with state-of-the-art unsupervised image and video stitching approaches. Specifically, for image quality assessment, we compared our method against the traditional image stitching approach LPC [16], learning-based image stitching methods UDIS [17] and UDIS++ [18], and the learning-based video stitching method StabStitch. The quantitative comparison results are presented in Table 1, where \"-/.\" represents PSNR/SSIM values, and \"-\" indicates failure cases for the corresponding category. The experimental results are summarized in Table 1.  
The proposed method outperforms existing approaches with clear numerical advantages. As shown in Table 1, it achieves an average PSNR/SSIM improvement of 17.07%/7.20% over UDIS and 15.07%/3.12% over UDIS++. Additionally, in terms of video stability (Table 2), it surpasses StabStitch by 5.3% on average, and in distortion reduction (Table 3), it shows a marginal yet consistent improvement over StabStitch++. The quantitative experimental results demonstrate that the proposed method outperforms current state-of-the-art unsupervised image and video stitching methods in both image quality and video stability. In the comparison experiments, our method was compared with traditional image stitching methods like LPC, learning-based methods such as \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Regular** & **Low-Light** & **Low-Texture** & **Fast-Moving** & **Average** \\ \hline LCP [16] & 24.22/0.812 & - & - & 23.88/0.813 & - \\ UDIS [17] & 22.66/0.771 & 29.59/0.899 & 28.26/0.874 & 20.91/0.738 & 25.98 (\%0)/0.833 (\%0) \\ UDIS++ [18] & 23.19/0.785 & 31.09/0.936 & 29.98/0.906 & 21.56/0.756 & 27.17 (4.58%)/0.859 (3.12%) \\ StabStitch [12] & 24.64/0.832 & 34.51/0.958 & 33.63/0.927 & 23.36/0.787 & 29.89 (15.07%)/0.899 (6.84%) \\ StabStitch++ [19] & 25.51/0.837 & 35.10/0.958 & 34.23/0.928 & 25.64/0.830 & 30.80 (18.57%)/0.897 (7.68%) \\ Ours & 24.81/0.833 & 34.60/0.957 & 33.71/0.929 & 24.17/0.801 & 30.41 (17.07%)/0.893 (7.20%) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparisons with image stitching methods on StabStitch-D dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Regular** & **Low-Light** & **Low-Texture** & **Fast-Moving** & **Average** \\ \hline StabStitch [12] & 0.8610 & 0.8560 & 0.8399 & 0.8605 & 0.8543 \\ StabStitch++ [19] & 0.8912 & 0.8812 & 0.8732 & 0.8748 & 0.8801 \\ Ours & 0.9086 & 0.8850 & 0.9059 & 0.8995 & 0.8997 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Stability comparison between StabStitch and the proposed method across four scenarios. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Regular** & **Low-Light** & **Low-Texture** & **Fast-Moving** & **Average** \\ \hline StabStitch [12] & 0.8649 & 0.8113 & 0.7995 & 0.8550 & 0.8327 \\ StabStitch++ [19] & 0.8660 & 0.8303 & 0.8018 & 0.8667 & 0.8412 \\ Ours & 0.8661 & 0.8399 & 0.7982 & 0.8630 & 0.8418 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Distortion comparison between StabStitch and the proposed method across four scenarios. UDIS and UDIS++, and the video stitching method StabStitch. The experimental data show that, in Regular, Low-Light, and Low-Texture scenes, our method achieves or surpasses other methods in terms of PSNR and SSIM. In the Fast-Moving scene, where rapid and irregular camera movements are present, our method exhibits higher alignment accuracy and stability, significantly outperforming StabStitch. This can primarily be attributed to the proposed end-to-end training approach and relative path computation strategy, which effectively reduces cumulative errors and maintains high image alignment precision.