# Fish Body Pattern Style Transfer Based on Wavelet Transformation and Gated Attention
## 4 Experiments
### Comparison
#### 4.3.3 Qualitative Results  
To ensure that the generated images maintained a high resemblance to the content image while balancing stylized textures, we conducted multiple evaluations by adjusting the style weight parameter. When the CCPL weight is set too low, it leads to a reduction in flow warp error, indicating decreased temporal coherence. Therefore, we kept the content weight and the CCPL weight fixed at \(10^{5}\). This allowed us to prevent excessive similarity leading to the loss of artistic realism or excessive difference resulting in poor texture retention. Figure 12 illustrates how different style weights impact the final stylized output. When the style loss is relatively high, the fish structure is well represented through the masks; however, much of the content image's texture is lost. Conversely, when the style loss is too low, the generated image retains only some color features from the style image, resulting in reduced detail. Thus, it is crucial to balance the weights of content loss, style loss, and CCPL to achieve significant stylization effects while maintaining structural fidelity. Figure 13 demonstrates the spatial control capabilities of FSSNet. By manipulating the segmentation masks for the foreground fish and background, our model allows users to apply different artistic styles independently to each region, offering greater customizability in artistic fish video generation. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Style** & **Index** & **CycleGAN [13]** & **AdaAttN [15]** & **SCTNet + Lecp [5]** &
\begin{tabular}{c} **FSSNet \\ **(Ours)** \\ \end{tabular} \\ \hline Blue and white & SSIM \(\uparrow\) & 0.607 & 0.620 & 0.582 & 0.632 \\ porcelain & PSNR \(\uparrow\) & 16.17 & 16.32 & 16.15 & 16.41 \\ Starry night & SSIM & 0.612 & 0.641 & 0.577 & 0.653 \\ PSNR & 16.43 & 16.68 & 16.35 & 16.64 \\ Mosaic & SSIM & 0.543 & 0.612 & 0.581 & 0.625 \\ PSNR & 16.06 & 16.44 & 16.21 & 16.53 \\ SSIM & 0.492 & 0.528 & 0.503 & 0.546 \\ PSNR & 15.64 & 16.29 & 15.88 & 16.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparison of SSIM and PSNR between different style transfer models. “\(\uparrow\)” means the higher the coefficient, the better the model. Figure 12: Relationship diagram of generated images based on blue and white porcelain style and style weight. (**a**) Content; (**b**) style weight = \(10^{5}\); (**c**) style weight = \(10^{4}\); (**d**) style weight = \(2\times 10^{3}\); (**e**) style weight = \(10^{3}\). To further validate the perceptual quality of our generated videos under different style transfer models, we conducted a user study. We selected four ornamental fish video clips from our dataset and applied three different style transfer algorithms using four representative paintings as reference styles. The four videos represented typical scenarios pertinent to our study, namely multiple fish with mutual occlusion, unstable background influences, rapid swimming of multiple fish, and steady swimming by a single fish. The partial frames of the generated video are shown in Figure 14.  
A total of 40 participants were recruited for the study, aged between 20 and 45, with a gender ratio of 1:1. The group included 10 computer vision researchers, 10 students majoring in marine science, and 20 general users with no relevant background, in order to ensure diversity in evaluation perspectives. In the survey, participants were asked to select the most preferred video from the four generated results each time. The results are shown in Table 3. Our model, with its spatial control mechanism, was the most preferred by participants across all evaluation metrics, demonstrating its effectiveness in balancing style adaptation and content preservation. For the stylization strength metric, FFSNet with spatial control achieved an impressive score of 56.25%. Figure 14: Stylish video display. (**a**) original video; (**b**) style image; (**c**) SCTNet + CCPL; (**d**) globally styled FFSNet; (**e**) locally styled FFSNet. Figure 13: FFSNet under spatial control.