# Fast Fourier Asymmetric Context Aggregation Network:
## 4 Experiments
### Comparison with Existing Approaches  
#### 4.5.1 Quantitative Comparison  
Tables 1-3 illustrate the quantitative comparison results between our method and existing approaches. It is worth noting, as discussed in [77], that, while PSNR and SSIM are effective in objectively measuring pixel-level image quality, their outcomes may sometimes contradict human perceptual judgments. For instance, certain meaningless blurs can lead to perceptual bias, which may not be adequately captured by PSNR and SSIM. Given that garment image generation tasks place greater emphasis on human perceptual judgment, our quantitative experiments prioritize perceptual quality metrics such as FID, LPIPS, and TOPIQ, using PSNR and SSIM as supplementary references for pixel-level image quality. Table 1 presents the results of the quantitative comparison experiments conducted on the SKFashion dataset. The results show that, under different texture patch size conditions, our method outperforms the baseline methods in terms of FID and LPIPS metrics while achieving comparable results in PSNR and SSIM. This indicates that the garment images generated by our method exhibit superior perceptual quality and also perform well in terms of pixel-level quality. As the texture patch size decreases, all methods experience some degree of performance fluctuation. Among them, PAINT [56], which models context based on a single texture patch, shows the poorest robustness to changes in texture patch size, resulting in the most significant metric fluctuations. ZITS [61] demonstrates the closest robustness to texture patch size variation compared to our method as it introduces positional encoding techniques to improve performance under small texture patch conditions. Additionally, it is noteworthy that, as shown in Table 4, our approach achieves superior performance while utilizing only approximately one-third to one-half of the computational parameters required by the baseline methods. This underscores the efficiency and lightweight nature of our method. \begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline
**Texture Patch Size** & **Method** & **FID\(\downarrow\)** & **PSNR\(\uparrow\)** & **LPIPS\(\downarrow\)** & **SSIM\(\uparrow\)** & **TOPIQ-FR\(\uparrow\)** & **TOPIQ-NR\(\uparrow\)** \\ \hline \multirow{4}{*}{Standard} & PAINT [56] & 9.03 & 22.46 & 0.10410 & 0.803 & 0.549 & 0.610 \\  & MST [79] & 5.93 & 24.21 & 0.06438 & 0.833 & 0.693 & 0.646 \\  & Albahar-MT [80] & 9.38 & 24.22 & 0.10733 & 0.839 & 0.521 & 0.545 \\  & EdgeConnect [81] & 5.55 & 24.67 & 0.06632 & 0.842 & 0.694 & **0.650** \\  & ZITS [61] & 5.30 & 24.78 & 0.06426 & **0.854** & 0.689 & 0.625 \\  & FCAN (Our) & **4.07** & **24.82** & **0.05556** & 0.848 & **0.732** & 0.646 \\ \hline \multirow{4}{*}{Medium} & PAINT [56] & 10.25 & 22.23 & 0.10131 & 0.797 & 0.514 & 0.599 \\  & MST [79] & 6.57 & 23.70 & 0.07012 & 0.821 & 0.615 & **0.645** \\  & Albahar-MT [80] & 12.31 & 23.15 & 0.12341 & 0.821 & 0.504 & 0.519 \\  & EdgeConnect [81] & 6.44 & 24.22 & 0.07434 & 0.831 & 0.668 & 0.640 \\  & ZITS [61] & 6.11 & 24.24 & 0.07195 & **0.840** & 0.670 & 0.618 \\  & FCAN (Our) & **4.74** & **24.36** & **0.06182** & 0.833 & **0.714** & 0.642 \\ \hline \multirow{4}{*}{Small} & PAINT [56] & 14.33 & 21.97 & 0.15231 & 0.788 & 0.442 & 0.545 \\  & MST [79] & 8.80 & 22.17 & 0.08973 & 0.798 & 0.597 & 0.628 \\  & Albahar-MT [80] & 14.65 & 22.52 & 0.14950 & 0.811 & 0.431 & 0.534 \\  & EdgeConnect [81] & 7.47 & 23.59 & 0.08842 & 0.814 & 0.601 & **0.647** \\  & ZITS [61] & 6.89 & 23.73 & 0.07922 & **0.830** & 0.651 & 0.614 \\  & FCAN (Our) & **5.53** & **23.95** & **0.06906** & 0.821 & **0.693** & 0.638 \\ \hline \hline \end{tabular}  
* In the quantitative experiments, \(\uparrow\) and \(\downarrow\) indicate that higher and lower values of the corresponding metric are better, respectively. The best metric results are highlighted in **bold black**, while the second-best results are underlined. The same notation is used throughout.  
\end{table} 
Table 1: Quantitative experimental results on the SKFashion dataset.