Automated Color Correction via True Color Database Comparison
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Solution Overview
Problem
Users face challenges in accurately correcting digital image colors due to varying lighting conditions and the need for manual input or additional tools like gray cards, making conventional color correction methods cumbersome and impractical.
Innovation Solution
An automated system that identifies objects in images using machine learning models, compares captured colors to true color information stored in a database, and applies color deltas to correct image colors, eliminating the need for manual input and additional tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual color correction methods are used, then color accuracy can be improved, but user effort and time consumption increase
Solution Approach 1:
The system automatically performs color correction by identifying objects in the image, comparing their captured colors to true color information from a database, and applying color deltas without requiring manual user intervention. This self-service approach resolves the contradiction by achieving accurate color correction while eliminating the time consumption and effort associated with manual methods.
Solution Approach 2:
The system pre-populates a database with true color information for various objects before the color correction process. This preliminary action enables the automated comparison and correction step to proceed efficiently without requiring users to manually input reference colors, thus improving both accuracy and speed.
2Ease of operation
If automated color correction is implemented, then user effort is reduced, but color accuracy may deteriorate
Solution Approach 1:
The system uses feedback by comparing captured color information against stored true color information in the database. This comparison generates color deltas that guide the correction process, ensuring that automated correction achieves accurate results by continuously referencing known true colors rather than making arbitrary adjustments.
Solution Approach 2:
The system replaces manual mechanical color correction operations with an automated computational process. Machine learning models automatically identify objects and the system automatically calculates and applies color corrections based on database comparisons, substituting user manual operations with automated algorithms that maintain or improve accuracy.
3Measurement precision
If traditional color correction tools like gray cards are used, then color reference accuracy is improved, but device complexity increases
Solution Approach 1:
The system uses digital copies of true color information stored in a database instead of physical reference tools like gray cards. These digital color references are copied from pre-stored data and applied through software processing, eliminating the need for physical equipment while maintaining color reference accuracy.
Solution Approach 2:
The system extracts color reference information directly from the image by identifying objects and retrieving their true color data from the database, rather than requiring external physical reference tools. This extraction approach simplifies the device requirements by using only the imaging device and computational resources.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can identify an object depicted in an image. True color information associated with the object is obtained from a true color database comprising true color information for a plurality of objects. A color delta associated with the object is determined based on the true color information and captured color information associated with the object. The image is modified based on the color delta.