Abstract:
Calibration of a target imager. Responses of the target imager and a reference imager to predetermined importance-weighted color sample data are modeled using a predetermined target and reference imaging attributes. A calibration for the target imager is determined using the modeled responses and a predetermined calibration for the reference imager.
Abstract:
A first number of color patches are printed with a target printing system to obtain a sparse color gamut characterization. A second number of color patches are printed with a reference printing system to obtain a reference color gamut characterization. The second number is greater than the first number. A dense color gamut characterization is generated with a transformation of the reference color gamut characterization to the sparse color gamut characterization. A color management resource can be generated for the target printing system from the dense color gamut characterization.
Abstract:
Example implementations relate to cross-calibration of imagers. For example, cross-calibration of imagers can include modeling a response of each of a plurality of imagers to a color sample, modeling a response of a composite the a plurality of imagers to the color sample based on the modeled response of each of the plurality of imagers to the color sample, generating generate a cross-calibration of the modeled response of each of the plurality of imagers to the modeled response of the composite of the plurality of imagers, and generating a calibration of the modeled response of the composite of the plurality of imagers to a response of a reference imager.
Abstract:
Calibration of a target imager. Responses of the target imager and a reference imager to predetermined importance-weighted color sample data are modeled using a predetermined target and reference imaging attributes. A calibration for the target imager is determined using the modeled responses and a predetermined calibration for the reference imager.
Abstract:
Example implementations relate to PRNU suppression. For example, PRNU suppression may include performing a calibration surface PRNU characterization using a scanning system, performing a document-based PRNU characterization using the scanning system, and determining a correction function for PRNU suppression for the scanning system based on the calibration surface PRNU characterization and the document-based PRNU characterization.
Abstract:
Example implementations relate to PRNU suppression. For example, PRNU suppression may include performing a calibration surface PRNU characterization using a scanning system, performing a document-based PRNU characterization using the scanning system, and determining a correction function for PRNU suppression for the scanning system based on the calibration surface PRNU characterization and the document-based PRNU characterization.
Abstract:
Example implementations relate to cross-calibration of imagers. For example, cross-calibration of imagers can include modeling a response of each of a plurality of imagers to a color sample, modeling a response of a composite the a plurality of imagers to the color sample based on the modeled response of each of the plurality of imagers to the color sample, generating generate a cross-calibration of the modeled response of each of the plurality of imagers to the modeled response of the composite of the plurality of imagers, and generating a calibration of the modeled response of the composite of the plurality of imagers to a response of a reference imager.