# A Method of Enhancing Silk Digital Printing Color Prediction through Pix2 Pix GAN-Based Approaches
## 3 Results
### Pix2 Pix GAN Based Color Prediction Model Results  
A Pix2 Pix GAN-based color prediction model based on two types of digital printed images on silk crepe satin and silk twill was constructed. The paired images totaled 2626, divided into a training set (2326 images) and a test set (300 images). Figure 6 shows the comparison between predicted images from the test set and original printed fabric images, as well as the color distribution in test sets. The color gamut of printed fabrics was preserved in the predicted images. After calculating with the CIEDE2000 color difference formula, the color data of the measured actual printed fabrics and the color data of the predicted digital printing image were compared. The color difference and contribution of lightness, chroma and hue differences are summarized in Table 2. The method predicts highly accurate colored images with an average \(\Delta\)E\({}_{00}\) of 2.37 for silk crepe satin and an average \(\Delta\)E\({}_{00}\) of 1.85 for silk twill. The standard deviations of \(\Delta\)E\({}_{00}\) are 1.50 and 1.04, respectively, indicating the method's robustness. Chroma differences are the biggest contrib \begin{table}
\begin{tabular}{l c c c c c c} \hline  & **Average \(\Delta\)E\({}_{00}\)** & \begin{tabular}{c} **Maximum** \\ **\(\Delta\)E\({}_{00}\)** \\ \end{tabular} & \begin{tabular}{c} **Standard** \\ **Deviation \(\Delta\)E\({}_{00}\)** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **\%\(\Delta\)L\({}_{00}\)** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **\%\(\Delta\)L\({}_{00}\)** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **\%\(\Delta\)L\({}_{00}\)** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **\%\(\Delta\)L\({}_{00}\)** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **\%\(\Delta\)H\({}_{00}\)** \\ \end{tabular} \\ \hline Silk crepe satin vs. Pantone TCX & 4.80 & 32.68 & 2.12 & 68.83\% & 15.65\% & 15.52\% \\ color card & 4.83 & 31.41 & 2.04 & 67.00\% & 16.73\% & 16.27\% \\ color card & & & & & & \\ \hline Silk crepe satin vs. silk twill & 1.78 & 28.23 & 0.84 & 97.29\% & 1.76\% & 0.95\% \\ \hline \end{tabular}
\end{table} 
Table 1: CIEDE2000 color differences and their components in percentages (%\(\Delta\)L\({}_{00}\), %\(\Delta\)C\({}_{00}\), %\(\Delta\)H\({}_{00}\)) between 2626 pairs of color cards and corresponding printed silk fabrics of silk crepe satin and silk twill, and 2626 color pairs of silk crepe satin and silk twill. Figure 6: Color gamut of comparison between predicted and original printed fabric images. The left panel presents a*, b* values and L*, hab values of the predicted color data of the silk crepe satin in red squares and actual color data of the printed silk samples in blue circles; the right panel presents a*, b* values and L*, hab values of the predicted color data of the silk twill in red squares and actual color data of the printed silk samples in blue circles. differences between prediction and actual printed fabrics (averages of 43.35% and 42.30%, respectively). Hue differences are second biggest contributor, at 36.96% and 38.96%. Unlike the percentage contribution seen in the PANTONE TCX color card and printed fabrics, lightness differences do not contribute significantly to the color differences. Figure 7 shows the individual color differences from the two different fabric prediction models. By binning the CIEDE2000 results of predicted images vs. actual fabric images using a 0.7 CIEDE2000 unit bin size, the resulting distribution plot also shows the number of predicted images below the 0.7 visual threshold for human observers with normal color vision to be 7% and 8% [23; 24]. The distribution of predictions with a \(\Delta\)E\({}_{00}\) below 2.1 are in the majority for both silk crepe satin and silk twill, for about 40% of the predictions of silk twill fall within color differences of 1.4 CIEDE2000 unit, indicating the model performs better than silk crepe satin, for which only 25% of the predictions fall below 1.4 CIEDE2000 unit.