Learning Device for Automatic Print Correction
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Solution Overview
Problem
In image printing, users face a heavy burden in manually setting correction information for achieving desired color and vividness, as existing methods require manual selection of correction modes and are inflexible, failing to account for individual user preferences.
Innovation Solution
A learning device that performs machine learning on a dataset associating images with correction information, allowing for the automatic generation of recommended correction information based on user-specific data, reducing the need for manual input and enhancing flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual setting of correction information is used, then printing quality can be controlled, but user burden increases and flexibility decreases
Solution Approach 1:
The system automatically performs correction information setting by acquiring multiple images with different correction information and generating combined correction information without requiring manual user intervention. The printing apparatus itself serves to set the correction parameters by processing the acquired images and determining optimal correction values.
Solution Approach 2:
The system acquires images that have already undergone correction processing with different correction information, then uses this feedback to determine optimal combined correction information. The correction settings are refined iteratively based on the acquired image results.
2Manufacturing precision
If manual setting of correction information is used, then printing quality can be controlled, but flexibility and personalization decrease
Solution Approach 1:
The system automatically adapts to user preferences by acquiring images and analyzing correction information without requiring manual input. The printing apparatus self-configures the correction parameters based on acquired image data, enabling personalized printing settings to be generated automatically.
Solution Approach 2:
The system performs preliminary acquisition of multiple images with different correction information before final printing. This preliminary data collection enables the system to pre-determine optimal combined correction information that reflects user preferences before actual printing occurs.
3Productivity
If automatic correction is performed using existing methods, then correction speed improves, but adaptability to user preferences decreases
Solution Approach 1:
The system uses feedback from acquired images with different correction settings to automatically determine optimal correction information. By analyzing the results of corrections applied to acquired images, the system adapts to user preferences while maintaining automated operation.
Solution Approach 2:
The printing apparatus automatically adapts to user preferences without requiring manual input or selection. The system self-configures correction parameters by processing acquired images and determining the best correction information based on the results.
Data Source
AI summary
A learning device includes an acquiring unit and a learning unit. The acquiring unit acquires an image and correction information designated for printing the image. The learning unit performs machine learning on recommended correction information that indicates correction content recommended for the print target image in printing the print target image, based on the data set in which the image is associated with the correction information.