# A Method of Enhancing Silk Digital Printing Color Prediction through Pix2 Pix GAN-Based Approaches
## 4 Discussion  
Color differences seen in PANTOME TCX color card and actual printed silk twill and silk crepe satin are common, due to problems in color transmittance through different devices, ink color deviations, machine parameterization, etc. Additionally, the style of digital inkjet print fabrics is determined by the style of the substrate fabric; the same design colors may have different visual effects on different substrates. Silk twill has a distinct oblique texture, while silk crepe satin has a strong gloss without an obvious texture, so the same design colors may have different visual effects on the two fabrics. Thus, our results showed that color reproduction from design to actual prints in manufacturing settings would lead to noticeable color differences. Using an image-based color prediction method, designers and manufacturers can potentially \"see\" what they will produce in the end by their particular manufacturing setting and thus be able to decide on achievable colors choices, reduce sampling/waste and even select a color on the computer screen that would result in the desired effect. Our experimental results show that Pix2 Pix GAN is a robust color prediction method in predicting color outcomes of digital inkjet printing on silk twill and silk crepe satin. The perceptible thresholds of color differences for average human observers with normal color vision can defined as \(\Delta\)E\({}_{00}\)\(\leq\) 0.7 [23; 24]. When \(\Delta\)E\({}_{00}\)\(\leq\) 0.7, the human eye usually has difficulty perceiving the difference between two colors. According to experimental results (Table 2), the average color differences for predicting silk crepe satin and silk twill are 2.372 and 1.846, respectively. About 40% of predictions of silk twill fall within color differences of 1.4 CIEDE2000 unit, while 25% of predictions of silk crepe satin fall below 1.4 CIEDE2000 unit. We suspect that, since the twill fabric has a uniform oblique texture on the surface and the light reflection is more uniform [25], the predicted color difference is smaller and the result is more accurate. From a method development point of view, color prediction methods based on deep learning may use deep neural networks to implement colorization of images. Such methods can use a large amount of color image data to train the network, enabling it to automatically learn how to add appropriate colors to grayscale images. The advantages of U-Net and Patch GAN in Pix2 Pix in our current setting may improve the quality and realism of the generated image in image-to-image transformation tasks. U-Net can effectively handle image segmentation tasks and retain important information about the content of the image. Patch GAN can evaluate the difference between the generated image and the target image in a more granular way, thereby improving the quality and realism of the generated image. Further optimization of the Pix2 Pix model is necessary to enhance prediction accuracy in areas where the current version exhibits suboptimal performance. Moreover, to offer more cost-effective application scenarios for the industry, additional optimization of the model training process is needed to ascertain the minimal input dataset required to attain both high accuracy and robustness. Future work would also include generating datasets using diverse methodologies and algorithm implementation to take in diverse sources of data. This study is an attempt to apply a modified Pix2 Pix model to silk digital print color prediction with different types of fabrics. The proposed methodology demonstrates a high degree of effectiveness in accurately forecasting the color outcomes of digital printing on silk, thereby offering valuable guidance to designers in optimizing resource utilization and streamlining the sampling process.