Abstract
In this work four different machine learning approaches have been implemented to perform the color space transformation between CMYK and CIELAB color spaces. We have explored the performance of Support-Vector Regression (SVR), Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Radial Basis Function (RBF) models to achieve this color space transformation, both AToB and BToA direction. The data set used for this work was FOGRA53 which is composed of 1617 color samples represented both in CMYK and CIELAB color space values. The accuracy of the transformation models was measured in terms of ΔE* color difference. Moreover, the proposed models were compared, in practical terms, with the performance of the standard ICC profile for this color space transformation. The results showed that, for the forward transformation (CMYK to CIELAB), the highest accuracy was obtained using RBF. While, for the backward transformation (CIELAB to CMYK), the highest accuracy was obtained with DNN.
Highlights
Color management is an essential task in several fields of research and industry, such as graphic design, photography, printing, and image processing, among others [13]
We explore the performance of Support-Vector Regression (SVR), Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Radial Basis Function Network (RBF) models to perform this color space transformation, both AToB and BToA directions
We have explored the performance of SVR, ANN, DNN, and RBF models to perform this color space transformation both AToB and BToA directions
Summary
Color management is an essential task in several fields of research and industry, such as graphic design, photography, printing, and image processing, among others [13]. CMYK is a devicedependent color space used in the printing industry due to its subtractive nature. It represents each color using four components: cyan, magenta, yellow, and black [14]. CIELAB is a device-independent color space derived from the previous CIE 1931 XYZ color space with the aim of creating a more uniform color space. It represents each color using three values, L* for lightness, a* for the green-red component, and b* for the blueyellow component. Like CIEXYZ, CIELAB is important in color management because it works as a profile connection space [11]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
