A layer configuration prediction method is provided and includes: a specimen production step of producing multiple specimens by depositing layers of a material in configurations different from each other; a specimen measurement step of performing, on each specimen, measurement to acquire a texture parameter corresponding to a texture; a learning step of causing a computer to perform machine learning of a relation between each of the specimens and the texture parameter; a setting parameter calculation step of calculating a setting parameter corresponding to the texture set to a computer graphics image; and a layer configuration acquisition step of providing the setting parameter as an input to the computer having been caused to perform the machine learning, and acquiring an output representing the layering pattern of layers of the material corresponding to the setting parameter.

The present invention relates to a method of generating “expanded color chart” representing various ink combination, for profiling a multi-color printing device or printer, using “reduced set of color chart”, printed using the printer, where a. The expanded color chart is computed by applying “Error Correction” function to the expanded color chart predicted using a theoretical model of color prediction for a given ink combinations in the expanded chart. b. Error Correction function is computed/modelled as function of the variance/difference observed between the color values predicted by the theoretical model and the actual values measured for the ink combinations in the printed “reduced set of color chart”.

Described herein is a computer-implemented method. The method includes: providing digital images and respective formulas for coating compositions with known pigments and/or pigment classes associated with the respective digital images, classifying, using an image annotation tool, for each digital image, each pixel, by visually reviewing the respective digital image pixel-wise, providing, for each digital image, an associated pixel-wise annotated image, training a first neural network with the provided digital images as input and the associated pixel-wise annotated images as output, making the trained first neural network available for applying the trained first neural network to at least one unknown input image of a target coating and for assigning a pigment label and/or a pigment class label to each pixel in the at least one unknown input image, and determining and/or outputting, for each unknown input image, a statistic of corresponding identified pigments and/or pigment classes, respectively.

Disclosed herein is a computer-implemented method, a respective device, and a non-transitory computer-readable medium. The method includes: obtaining color values, texture values and digital images of a target coating, retrieving from a database one or more preliminary matching formulas based on the color and/or texture values obtained for the target coating, determining sparkle points within the respective obtained images and within the respective images associated with the one or more preliminary matching formulas, creating subimages of each sparkle point from the respective images, providing the created subimages to a convolutional neural network, the convolutional neural network being trained to correlate a respective subimage of a respective sparkle point with a pigment and/or pigment class, and determining, based on an output of the neural network, at least one of the one or more preliminary matching formulas as the formula(s) best matching the target coating.

First relational equations which represent characteristics of respective sample colors are obtained, and for the respective sample colors, prediction values of spectral characteristics of characteristics-acquired gradation values for a prediction target color are obtained using the first relational equations. Difference values between the prediction values and actual measurement values are obtained, and a sample color for which a minimum difference value is obtained is selected as a reference color. A second relational equation that represents characteristics of the reference color is obtained, and a prediction value of spectral characteristics of a prediction target gradation value for the prediction target color is obtained using the second relational equation.

An image processing apparatus includes a processor configured to generate a second color chart from a first color chart. The second color chart includes multiple color samples selected from multiple color samples included in the first color chart, and the second color chart is generated in such a manner that a color sample included in the second color chart is located in the second color chart at a position at least maintaining a partial-adjacency relationship in the first color chart.

A method for printing at least a first and a later print job includes receiving a first print job containing first image data of one or more first images to be printed in a printable area and a later print job containing later image data associated with a colour, determining a first sub-area of said printable area where no image of the first images is to be printed, using the first image data; printing the one or more first images in the printable area and at least one first control patch in the first sub-area, using for said at least one first control patch at least one colour representative for the colour associated with the later image data, measuring the colour of the printed at least one first control patch, and using the measured colour of the at least one first control patch for controlling the later print job.

In some examples, a system receives image data for printing by a printer, and for a given colorant of a plurality of colorants to be used to print an image based on the image data, selects a plurality of different types of masks to use when dispensing printing fluid drops of the given colorant when printing the image. The system generates control data for printing the image using the selected plurality of different types of masks.

A printing system is disclosed. The printing system includes at least one physical memory device to store calibration logic and one or more processors coupled with the at least one physical memory device to execute the calibration logic to perform uniformity compensation, including receiving print image measurement data corresponding to primary color markings and secondary color markings printed by pel forming elements, wherein each of the pel forming elements is associated with one of a plurality of primary colors, generating a first set of transfer functions based on the print image measurement data, wherein the first set of transfer functions includes a transfer function for each of the pel forming elements associated with a primary color, generating a second set of transfer functions based on the print image measurement data, wherein the second set of transfer functions includes transfer functions for each of the pel forming elements associated with a plurality of secondary colors, generating a set of inverse transfer functions for each of the pel forming elements based on the first set of transfer functions and the corresponding second set of transfer functions and generating a uniformity compensated halftone design based on the set of inverse transfer functions and a first halftone design.

A printing system is disclosed. The printing system includes at least one physical memory device to store calibration logic and one or more processors coupled with the at least one physical memory device to execute the calibration logic to perform uniformity compensation, including receiving print image measurement data corresponding to primary color markings and secondary color markings printed by pel forming elements, wherein each of the pel forming elements is associated with one of a plurality of primary colors, generating a first set of transfer functions, wherein the first set of transfer functions include a transfer function for each of the pel forming elements associated with a primary color, generating a second set of transfer functions, wherein the second set of transfer functions include transfer functions for each of the pel forming elements associated with a plurality of secondary colors and generating a set of uniformity compensated transfer functions, wherein the set of uniformity compensated transfer functions include a transfer function for each of the pel forming elements based on the first set of transfer functions and the corresponding second set of transfer functions.