Scanner Color Profile Association via Spatial Image Analysis
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Solution Overview
Problem
Existing scanner calibration methods require manual identification of the marking process used to form an image on a substrate, which is inaccurate and inefficient, as they depend on user knowledge and additional resources or sensors.
Innovation Solution
The system automatically identifies the marking process by analyzing spatial characteristics of the scanned image data without additional sensors or resources, differentiating between continuous tone and binary processes, and specific types like inkjet, xerographic, and lithographic processes using local variations, halftone dot periodicity, and frequency characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of marking process is used, then user can select appropriate calibration transformation, but the process is inaccurate and inefficient depending on user knowledge
Solution Approach 1:
The system performs self-identification of the marking process by automatically analyzing spatial characteristics of the scanned image data. The scanner independently determines whether the image was created by photographic, inkjet, xerographic, or lithographic processes without requiring user intervention or external resources, thereby improving both accuracy and operational simplicity.
Solution Approach 2:
The patent replaces the manual mechanical process of user identification with an automated computational analysis system. Instead of users manually examining and identifying marking processes, the system uses spatial attribute analysis algorithms to automatically detect and classify the marking process based on image data characteristics.
2Measurement precision
If additional spectral channels or sensors are used for automatic identification, then marking process detection accuracy improves, but device complexity and cost increase
Solution Approach 1:
The patent extracts and utilizes only the necessary spatial information already present in the standard scanned image data. By focusing on spatial attributes such as periodicity, frequency, and texture patterns inherent in the image itself, the system eliminates the need for additional spectral channels or sensors, maintaining device simplicity while achieving accurate marking process identification.
Solution Approach 2:
The spatial attribute analysis system serves multiple functions using the same standard scanner sensors: it performs both常规图像采集 and marking process identification. This multi-functionality eliminates the need for dedicated additional sensors, as the existing scanner hardware is leveraged to extract both image data and spatial characteristics for automated classification.
3Manufacturing precision
If manual calibration transformation selection is used, then appropriate color correction can be applied, but productivity is reduced due to manual intervention
Solution Approach 1:
The system performs preliminary identification of the marking process automatically before the user needs to select calibration transformations. By pre-determining the marking process type through spatial attribute analysis, the system automatically applies the appropriate calibration transformation, eliminating the need for manual selection and accelerating the scanning workflow while maintaining calibration accuracy.
Solution Approach 2:
The system uses feedback from spatial characteristic analysis to automatically adjust and select the appropriate calibration transformation. The spatial attributes of the scanned image provide feedback about the marking process type, which the system uses to autonomously configure the correct calibration parameters, thereby improving both accuracy and productivity by eliminating manual intervention.
Data Source
AI summary
Methods and systems used to associate color calibration profiles with scanned images based on identifying the marking process used for an image on a substrate using spatial characteristics and/or color of the image. Image types which are classified and identified include continuous tone images and halftone images. Among halftone images separately identified are inkjet images, xerographic images and lithographic images. Locally adaptive image threshold techniques may be used to determine the spatial characteristics of the image.