# End-to-End Online Video Stitching and Stabilization Method Based on Unsupervised Deep Learning
## 3 Experimental Results and Analysis
### Ablation Experiments  
To further explore the contribution of each module and strategy to overall system performance, we conducted a series of ablation experiments. In the experiments, \"original stitching\" refers to stitching each frame individually without employing the joint smoothing strategy. \"Single-input smoothing\" involves using the original unstable path as input in the trajectory smoothing module, while \"dual-input smoothing\" uses both the original and historical smoothed paths as inputs. Table 4 presents the ablation experimental results on alignment, distortion, and stability metrics. Figure 4: Experiment 1: Qualitative comparison experiment. The results indicate that, while maintaining image stitching quality and a low distortion rate, the introduction of historical smoothed trajectories as additional input significantly improves the overall stability rate. This demonstrates that historical smoothing information plays a critical role in transitioning between newly generated frames and previously stable frames, effectively reducing cumulative errors and enhancing the overall stability of the video. Moreover, to evaluate the impact of the relative path and absolute path strategies on system performance, we conducted a comparative experiment, with the results shown in Table 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Basic** & **Single Input** & **Dual Input** & **Alignment \(\uparrow\)** & **Distortion \(\uparrow\)** & **Stability \(\uparrow\)** \\ \hline ✓ & & & 30.55/0.893 & 0.8411 & 0.8994 \\ ✓ & ✓ & & 30.65/0.894 & 0.8395 & 0.8842 \\ ✓ & & ✓ & 30.41/0.893 & 0.8418 & 0.8997 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation studies on alignment, distortion, and stability. (“\(\uparrow\)” means higher values are better for the metric). Figure 5: Experiment 2: Qualitative comparison experiment. The experimental results reveal that the relative path calculation method can significantly reduce cumulative errors while ensuring alignment accuracy, further improving video stability. Specifically, in terms of alignment and distortion rates, both path strategies perform similarly. However, the relative path method shows a more significant advantage in stability metrics, proving its effectiveness in suppressing error accumulation during the stabilization of long-sequence videos. To comprehensively evaluate the impact of key hyperparameters on model performance, we conducted a series of ablation and sensitivity experiments. Specifically, we analyzed the influence of trajectory smoothing coefficients (\(\alpha_{\text{s}}\), \(\beta_{\text{s}}\), and \(\gamma_{\text{s}}\)) by independently varying each parameter while keeping the others fixed at their default values. The experimental results, presented in Tables 6-8, indicate that lower values of \(\alpha\)s lead to insufficient smoothing and noticeable jitter artifacts, while overly large values introduce excessive smoothing that compromises structural consistency and increases distortion. Similarly, increasing \(\beta_{\text{s}}\) enhances the fidelity of the estimated paths but may introduce slight reductions in temporal stability. Adjusting \(\gamma_{\text{s}}\) affects the temporal coherence, particularly near sequence boundaries; however, excessive values can lead to performance degradation in overall visual quality. These observations highlight the importance of careful tuning to maintain a balanced trade-off among visual stability, spatial alignment, and minimal distortion. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\alpha_{\text{s}}\) & **Stability \(\uparrow\)** & **Distortion \(\uparrow\)** & \
\begin{tabular}{c} **Alignment** \\ **(PSNR/SSIM) \(\uparrow\)** \\ \end{tabular} &
\begin{tabular}{c} **Description** \\ \end{tabular} \\ \hline
5 & 0.8832 & 0.8435 & 30.39/0.892 &
\begin{tabular}{c} Insufficient smoothing; \\ trajectory jitter. \\ Balanced. \\ Over-smoothed; slight \\ increase in distortion. \\ More over-smoothed; \\ increased distortion. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}