ABSTRACT To improve CMYK‐to‐CIELAB forward characterization under nonlinear and boundary‐sensitive conditions, this study proposes an adaptive color mapping network (ACMN) that integrates perceptual‐gated RBF fusion with CNN–MLP residual correction. A CIEDE2000‐guided iterative reweighting strategy is used to improve the perceptual consistency of the interpolation baseline, while a lightweight residual branch captures remaining local nonlinearities in CIELAB space. A continuous gate is further introduced to emphasize elevated‐risk regions without hard regional switching. On the FOGRA39 dataset, ACMN achieves a mean of 0.217 and a maximum of 1.064, with improved upper‐tail error behavior relative to standard RBF. Additional continuity and cross‐dataset results further support the robustness of the proposed method. The proposed framework provides a practical solution for high‐precision and perceptually reliable printer characterization.
Zhou et al. (Thu,) studied this question.