# Fish Body Pattern Style Transfer Based on Wavelet Transformation and Gated Attention
## 4 Experiments
### Comparison
#### 4.3.4 Ablation  
As illustrated in Figure 15, compared to traditional convolutional downsampling, the DWT module significantly enhances the capture of textural details by preserving the frequency sub-bands.  
To assess the computational complexity of our model, we calculated the total number of parameters responsible for style transfer and compared them with those of FNST. To simulate real-world working conditions, we used 1920 \(\times\) 1080 resolution three-channel images to compute FLOPs. For FPS measurements, we processed 100 frames of 1920 \(\times\) 1080 images for each model, repeating the computation three times to obtain the average FPS. The selected stylized method was spatial control. As shown in Table 4, the introduction of DWT contributes to a reduction in parameters, while the addition of CBAM increases both the parameter count and FLOPs. We enhanced the model's expressiveness with a minimal impact on overall runtime efficiency.  
To evaluate the temporal consistency improvements of our model on video frames, we compared the flow warping error among different models following the definition in [36]. For this experiment, we selected three long ornamental fish videos from our fish video database, containing occlusions and fast-swimming fish to test challenging scenarios. To ensure consistency across all tested models, the output videos were configured to 10 FPS, following the default setting in [5]. The video resolution was set to the original 1920 \(\times\) 1080 pixels. As shown in Table 5, our FSSNet model demonstrates significant improvements, achieving a flow warping error enhancement of 5.862-5.443 compared to the baseline models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Style Model** & **Params (M)** & **FLOPs (GFLOPs)** & **FPS** \\ \hline Baseline & 1.679 & 321,010 & 1.225 \\ Baseline + DWT & 1.628 & 310,393 & 1.200 \\ Baseline + DWT + CBAM & 1.639 & 312,155 & 1.198 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of model complexity and running performance.  
Figure 15: Comparison of the effects of different modules. (**a**) Content image. (**b**) Style image. (**c**) Baseline. (**d**) Baseline + DWT. (**e**) Baseline + DWT + CBAM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stylized Method** & \begin{tabular}{c} **User** \\ **Preference** \\ \end{tabular} & \begin{tabular}{c} **Temporal** \\ **Consistency** \\ \end{tabular} & \
\begin{tabular}{c} **Stylization** \\ **Strength** \\ \end{tabular} &
\begin{tabular}{c} **Content** \\ **Fidelity** \\ \end{tabular} \\ \hline SCTNet + \(L_{cap}\) & 18.75 & 6.25 & 12.5 & 12.5 \\ Global FFSNet & 25 & 25 & 18.75 & 25 \\ Local FFSNet & 25 & 31.25 & 12.5 & 18.75 \\ FFSNet with spacial control & 31.25 & 37.5 & 56.25 & 43.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Generated video user preference survey results. The user preference results show that participants selected the most aesthetically pleasing video. Temporal consistency is the degree of flickering in stylized videos. Stylization strength shows how well the generated video aligns with the reference style. Content fidelity means image sharpness and texture quality in the transformed videos.