An example of a color management module is disclosed. The color management module is to generate a mapping from an incoming color space to a printer color space by a color pipeline. In the generation of the mapping, the color pipeline is adjusted so that the mapping of a target color associated with a first composition further includes adding a quantity of a second composition to the mapping of the target color. The second composition comprises a lower visibility than the first composition.

A laser engraving apparatus (100) is calibrated using a number of predetermined calibration images (12). The calibration images (12) are engraved on a predefined substrate surface (14, 15) of a substrate (16) by varying only a single laser parameter. Based on measured colour values of the engraved calibration images, a relationship between said colour values and a predefined laser parameter value, for example, a predefined laser power, is established. This relationship is then used to generate a map for mapping a grayscale value of an input image to a grayscale value of an output image, which is then engraved on the substrate (16) while varying the same laser parameter that was varied during the calibration.

An information processing apparatus storing a machine-learned model that learned, by machine learning, a relationship between a type of a printing medium, an amount of a coloring material on the printing medium per unit area, and an image printed on the printing medium; and estimating, based on a selection information and a imaging information, by using the machine-learned model a limit value indicating a maximum value or a minimum value of an amount of the coloring material to be used in printing on the printing medium by the printing section per unit area; and creating, by using the limit value, a color conversion profile including information regarding mapping between a coordinate value in a color space and an amount of the coloring material.

An information processing apparatus configured to store a machine-learned model that learned, by machine learning, a relationship between a type of a printing medium, an amount of a coloring material on the printing medium per unit area, and an image including a gradation image and printed on the printing medium; and estimate, based on a selection information and a imaging information, by using the machine-learned model, a limit value indicating a maximum value or a minimum value of an amount of the coloring material to be used for printing on the printing medium by the printing section per unit area; and create, by using the limit value, a color conversion profile including information regarding mapping between a coordinate value in a color space and an amount of the coloring material.

An example system for thermal energy determination can include a first controller comprising a processor and a non-transitory machine-readable medium (MRM) communicatively coupled to the processor. The non-transitory MRM can include instructions executable by the processor to cause the processor to receive relative humidity information of an environment of a thermal printing device, determine a colormap to a print media of the thermal printing device based on the relative humidity, and determine a particular thermal energy to apply to the print media based on the determined colormap.

An image processing system includes an image forming apparatus, and an image processing apparatus connected to the image forming apparatus. The image forming apparatus forms an image, acquires a chromaticity value that is a result of measurement of a sheet on which the image is formed with use of a sensor in which a characteristic of a light source is fixed, and transmits the acquired chromaticity value to the image processing apparatus. The image processing apparatus receives the transmitted chromaticity value, specifies a condition when the result of the measurement using the sensor is acquired, and converts the received chromaticity value with use of the specified condition and information regarding an amount of an optical brightening agent contained in the sheet on which the image is formed.

An information processing apparatus configured to store a machine-learned model that learned, by machine learning, a relationship between a type of a printing medium, an amount of a coloring material on the printing medium per unit area, and an image including a linear image and printed on the printing medium; and estimate, based on a selection information and a imaging information, by using the machine-learned model, a limit value indicating a maximum value or a minimum value of an amount of the coloring material to be used for printing on the printing medium by the printing section per unit area; and create, by using the limit value, a color conversion profile including information regarding mapping between a coordinate value in a color space and an amount of the coloring material.

An information processing apparatus includes a processor configured to: obtain characteristic values including smoothness and basis weight of a recording medium; and identify the type of the recording medium on the basis of the smoothness and the basis weight.

Relative calibration is used to generate tone reproduction curves (TRCs) at multiple printing devices. A first calibration is performed for a first paper at a first printing device to generate a first TRC. A second calibration is performed for the first paper at a second printing device to generate a second TRC. A third calibration is performed for a second paper at the first printing device to generate a third TRC. A device difference dataset is determined using the first TRC and the second TRC. A paper difference dataset is determined using the first TRC and the third TRC. The device difference dataset and the paper difference dataset are used to determine a fourth TRC for the second paper at the second printing device, thereby eliminating the need for further calibration operations.

A laser engraving apparatus (100) is calibrated using a number of predetermined calibration images (12). The calibration images (12) are engraved on a predefined substrate surface (14, 15) of a substrate (16) by varying only a single laser parameter. Based on measured colour values of the engraved calibration images, a relationship between said colour values and a predefined laser parameter value, for example, a predefined laser power, is established. This relationship is then used to generate a map for mapping a grayscale value of an input image to a grayscale value of an output image, which is then engraved on the substrate (16) while varying the same laser parameter that was varied during the calibration.