Study of color reproduction in pigment digital printing
Source abstract
Abstract
In pigment ink digital printing, the color difference between the original and the printed product can be visible due to the varied color gamut of devices and the impact of fabric characteristics on the color-presenting effect. To improve the color reproduction accuracy of digital printing, two methods, the full-process optimized (FPO) method and the neural network aided original adjustment (NNAOA) method, were proposed to reduce printing color difference during the color-transferring process. The FPO method, based on accounting for the fabric type and the characteristics of the printing machine, was proposed by optimizing the printing parameter settings in the color management process. Here, the optimal single-channel ink limit was determined and dot gain was compensated using the inverse function correction method, and then the International Color Consortium profile was generated. After using the FPO method, the color difference between the printed sample and the original was reduced from 20.8 to 5.254 compared with performing the original color management. To reduce the reliance on personal experience in color adjustment, the NNAOA method was proposed and implemented for 20 color patches with the largest color differences in the FPO method, allowing the color of the originals to be adjusted with the establishment of the general regression neural network model. This method predicted the input CMYK values of the original using measured L*a*b* values for the printed sample. The results showed that the color difference of the 20 color patches was reduced from 8.54 to 6.60. Consequently, both methods offered distinguishable improvements in the color reproduction effect.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Color Science and Applications · Color perception and design · Dyeing and Modifying Textile Fibers
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Textiles, Donghua University, Key Laboratory of Textile Science and Technology, Ministry of Education, China
Shanghai Belt and Road Joint Laboratory of Textile Intelligent Manufacturing, China
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