# Dual-Modality Cross-Interaction-Based Hybrid Full-Frame Video Stabilization
## 4 Experiment
### Result
#### 4.3.2 Quantitative Results  
To quantitatively assess the extent of improvements made against the primary issues arising during video stabilization, we analyzed these improvements using the Stability score (S), Distortion value (D), and Cropping ratio (C) metrics. The Stability scores range  
Figure 8: Qualitative analysis of our result on specific regions: (**left**) Tree, (**center**) Brick, (**right**) Sand regions.  
Figure 7: Qualitative comparison results (category: Stairs).  
between zero and one, with values closer to one indicating a better quality of stabilization result. Distortion values also range between zero and one, with values closer to one signifying less flutter and more visual comfort. Cropping ratios, similarly, range between zero and one, with values closer to one indicating lesser cropping compared to the original footage. Experimental results are presented in Table 1 and show that our results employing IMU sensor for motion compensation as well as applying neural rendering increased the Cropping ratio by 36.2% per category (Stairs, Walk, Run, and Turn) compared to the L1 path method [4], generating full-frame images (see Figure 9). Secondly, our method produced videos with reduced visual artifacts, with a Distortion value increasing by 0.4-7.5% per category compared to the PCA-flow-based video stabilization method [8]. Our method obtained a better Stability score, which indicated more stable footage. Notably, traditional techniques such as the L1 path method, despite a good Stability score, suffered from a significant lower Distortion value and Cropping ratio due to nonlinear wobble and warping, leading to poor visual quality.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Stability (S) \(\uparrow\)** & **Distortion (D) \(\uparrow\)** & **Cropping (C) \(\uparrow\)** \\ \hline L1 path & 0.890 & 0.636 & 0.734 \\ PCA flow & 0.786 & 0.896 & 1.000 \\ NR & 0.776 & 0.940 & 1.000 \\ DIFRINT & 0.786 & 0.911 & 0.962 \\ Ours & 0.815 & 0.924 & 1.000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparison according to the average result of all four categories. The first rank is marked in red color, while the second rank is marked in blue color.  
Figure 9: Quantitative comparison using four different categorical data.  
Furthermore, we employed metrics such as the LPIPS, SSIM, and PSNR to quantitatively evaluate the extent of improvement in visual quality resulting from the video stabilization. LPIPS values closer to zero reflect a better perceptual similarity and alignment with human perception, especially for images with fine changes or complex textures. SSIM values closer to one suggest a greater similarity between two images, while the PSNR, used to measure the quality between the original and compressed images, indicates better image quality with higher values. The experimental results, utilizing the data from the \"Stairs\" category and evaluating the methods based on patches such as \"Tree\", \"Brick\", and \"Sand\" (see Figure 8), showed our method outperforming all methods in LPIPS, SSIM, and PSNR metrics for \"Tree\" (see Table 2). SSIM and PSNR values notably dropped for the L1 path and PCA-flow-based methods when adopting the IMU sensor compensation, possibly due to the preprocessing for motion compensation with the sensor output before applying the algorithm, leading to pixel value discrepancies in the input frames due to distortion.  
However, with respect to the computational efficiency, our method prioritized compensation using the IMU sensor and underwent processes involving PCA flow and neural rendering, resulting in a limitation of the extended runtime. As faster methods, the L1 path and DIFRINT methods ranked first and second, respectively (as shown in Table 3). Consequently, research conducted to date has primarily focused on verifying the potential for performance. Future efforts will necessitate improvements in lightweight and speed enhancements.