Dynamic Color Calibration via Sensor Feedback
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Solution Overview
Problem
Current color printing technologies lack flexibility in calibration and characterization processes, requiring a complete recalibration when parameters such as media, ink, or environmental conditions change, which is time-consuming and inefficient, especially when dealing with new media or temperature variations.
Innovation Solution
A method for fine-tuning model parameters in color printing using a database-driven approach that measures a small number of patches, allowing for dynamic adjustment of presets based on measured physical properties, enabling a combination of generic and custom settings for optimal calibration and characterization without the need for extensive recalibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a complete calibration is performed for a dedicated configuration, then color accuracy is improved, but the process becomes time-consuming and inflexible when parameters change
Solution Approach 1:
The system performs preliminary actions by pre-calibrating with generic presets and storing baseline colorimetric data before actual printing. This preliminary calibration provides a starting point that can be quickly adjusted later, avoiding the need to perform complete calibration from scratch when parameters change.
Solution Approach 2:
The calibration system transitions from a static complete calibration approach to a dynamic fine-tuning approach. The system measures a small number of patches and dynamically adjusts the generic preset parameters based on actual measurements, allowing rapid adaptation when media, ink, or environmental conditions change.
2Measurement precision
If complete recalibration is performed when parameters change, then color accuracy is maintained, but productivity decreases
Solution Approach 1:
Instead of performing complete recalibration, the system applies partial action by measuring only a small number of patches (significantly fewer than traditional calibration) and using only the necessary adjustments to fine-tune the generic preset. This partial measurement approach maintains color accuracy while dramatically reducing the time required, thus improving productivity.
3Productivity
If generic presets are used without fine-tuning, then calibration speed is improved, but color accuracy for specific media decreases
Solution Approach 1:
The system implements feedback by measuring actual colorimetric data from printed patches and using this measurement feedback to fine-tune the generic preset parameters. The measured values are compared against expected values, and the preset is adjusted accordingly, ensuring color accuracy for specific media while maintaining the speed advantage of starting with a generic preset.
Solution Approach 2:
The system changes parameters by adjusting the generic preset parameters based on measured patch data. Instead of using fixed generic values, the system modifies parameters such as ink densities, halftone screens, and color transformation matrices to match the specific characteristics of the media being used, thereby achieving both speed and accuracy.
Data Source
Figure 1A~1B
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AI summary
Color prediction for color printing is performed in response to loading media into a printer and initiation of a calibration sequence, using one or more sensors to measure physical properties of the media. Based at least upon the measured physical properties, an optimal preset is identified in a database and the optimal preset is loaded into the printer as a starting calibration. A difference from values in the optimal preset loaded into the printer and those of the measured physical properties is determined and the printer prints a chart. The sensors measure the chart and the measurements of the chart are used to fine tune the optimal preset. The fine tuned preset is then saved as a new media profile.