J. Eur. Opt. Society-Rapid Publ. 22, 45( 2026) 441
inscribed and the process carries on towards the next iteration.
2.5 Gamut mapping
When reproducing images containing colors that fall outside the achievable color gamut of a given printing process, it is necessary to adapt the image colors to the available palette. This process, known as gamut mapping, follows the approach introduced by Chosson and Hersch [ 28 ]. The procedure starts with the definition of a set of primary colors, including black, white, and chromatic primaries, which define the accessible color palette. To ensure consistent color appearance under varying viewing conditions, a chromatic adaptation transform is applied [ 43 ]. In practice, the tristimulus values of the palette are converted from their original white point to the D65 standard illuminant using the CAT02 chromatic adaptation model [ 44 ], as implemented in the colour-science library. This transformation maps the palette white( scaled by an adaptation factor) to D65 and adjusts all other colors accordingly. A lightness soft-compression is then performed to match the luminance range of the image to that of the available palette [ 45 ]. In parallel, the colors of the palette are transformed such that the gray axis defined by its black and white points aligns with the gray axis of the CIE 1976 L * a * b * color space. This alignment ensures a consistent treatment of achromatic components during the mapping process. Finally, a chroma soft-compression is applied to the image colors to constrain them within the available gamut while preserving, as much as possible, their perceived saturation. These successive transformations enable the selection of colors that provide the most faithful visual reproduction of the original image within the limitations of the accessible color space.
However, gamut mapping does not overcome the intrinsic limitations of the achievable color space. Improving the native color gamut therefore becomes a critical objective, which requires identifying laser processing conditions that maximize the accessible color space.
3 Results
In this section, a comparison on how much the GA, BO as well as random exploration strategies increase the gamut size and the printing quality. The comparison were done using“ replay experiments” analysis,“ live analysis” and by comparing the final images with both methods.
3.1 Replay experiments
A postmortem analysis was carried out to evaluate the speed with which each technique reached the total gamut. To do so, as many parameter combinations as possible were printed on the two types of samples and the colors were measured in different observation modes( described earlier). To ensure a fair comparison between the explorations, the GA, BO, and random explorations start with the same set of parameters, determined by a set seed. The use of
Figure 4. Example of parameter selection based on predicted color distributions in the CIELAB color space. The a * and b * axes represent chromaticity coordinates, while the shaded blue polygon denotes the color gamut already obtained without adding new parameters. The colored ellipses( red for P 1, green for P 2, and purple for P 3) indicate the 95 % confidence regions of the predicted color positions, showing where the resulting color is most likely to lie for each parameter. Dots within the ellipses represent individual simulated color coordinates, and the blackedged circles correspond to the mean predicted color for each parameter. The outer black line represents the current achievable gamut, while the green and purple outlines illustrate possible extensions of the gamut if the actual color corresponds to predicted points outside the existing region. This example suggests that P 2 is the most promising choice( most likely to expand the gamut), followed by P 3( large uncertainty) and P 1( unlikely to expand the gamut).
LHS ensures a well-distributed initial sampling of the parameter space, which explains why the starting gamut volumes are relatively consistent and not drastically different across various seeds. The exploration was simulated according to each method. Random selection sampled new candidates uniformly at random. GA suggested new parameters and the closest non inscribed parameter( using Euclidian norm over power, speed, repetition rate space) in the database was selected. For the BO, as the proposition is done through an acquisition function, only the value for the parameters present in the dataset was evaluated. The performance was assessed by evaluating the rate of variation in the gamut volume( Fig. 5), and the number of points expected to reach a certain proportion of the total gamut. The left plot corresponds to a sample made by magnetron sputtering in scattering reflection mode, and the right one to the mesoporous film sample in backside reflection mode. Similar results are observed in other modes for the same samples as shown in Appendix C. BOmethoddidoutperform GA, achieving a faster convergence toward the maximum attainable gamut volume with fewer of the inscribed parameters were required to reach the maximum gamut.