Ambient Light Type Estimation for Accurate Color Matching
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Solution Overview
Problem
Existing color matching technologies face challenges in accurately handling and processing large volumes of spectral data for ambient light, leading to either excessive data storage requirements or reduced accuracy when using XYZ tristimulus values, particularly when dealing with various types of ambient light conditions.
Innovation Solution
An information processing apparatus that acquires and compares spectral data of ambient light with reference data to estimate the ambient light type, utilizing a normalization process and weighting function to accurately identify the type and calculate color temperature, thereby reducing data volume and improving matching accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral data of ambient light is used for color matching, then color matching accuracy is improved, but data volume and storage requirements increase excessively
Solution Approach 1:
The patent extracts only the essential spectral data points needed for accurate color matching by comparing against reference ambient light spectra. Instead of storing and processing complete spectral data for all ambient conditions, the system extracts and stores only the reference spectral signatures (e.g., daylight, fluorescent, LED, incandescent) and uses these to identify and process ambient light conditions, thereby reducing data volume while maintaining accuracy.
Solution Approach 2:
The patent changes the representation of ambient light from full spectral data to identified light types/categories. By transforming the continuous spectral measurement into discrete ambient light type identification (e.g., classifying as 'daylight', 'fluorescent', 'LED'), the system reduces data complexity and storage requirements while preserving the essential information needed for accurate color matching under different ambient conditions.
2Quantity of substance
If XYZ tristimulus values are used for color matching, then data volume is reduced, but color matching accuracy deteriorates due to inability to distinguish between different ambient light types with same XYZ values
Solution Approach 1:
The patent introduces ambient light type identification as an intermediary step between spectral measurement and color matching. Instead of directly using XYZ values or full spectral data, the system first identifies the ambient light type (e.g., daylight, fluorescent, LED) based on spectral characteristics, then uses this identification to select appropriate color matching parameters and reference data, thereby achieving both data efficiency and accuracy.
Solution Approach 2:
The patent segments the ambient light conditions into distinct types or categories based on their spectral characteristics. By dividing the continuous spectrum into identifiable light types (daylight, fluorescent, LED, incandescent), the system can process each type with appropriate reference data and parameters, achieving accurate color matching without requiring processing of all possible spectral variations simultaneously.
3Measurement precision
If spectral data of object color is stored for accurate color matching, then color matching accuracy is improved, but data management and processing complexity increase
Solution Approach 1:
The patent extracts and stores only the reference spectral data for various ambient light types rather than storing spectral data for all possible object colors. By focusing on storing reference ambient light spectra (daylight, fluorescent, LED, etc.) and using these as references, the system achieves accurate color matching without the need to manage extensive spectral libraries of object colors, thereby reducing data management complexity.
Data Source
AI summary
An information processing apparatus includes a unit configured to acquire spectral data of ambient light to be estimated, a unit configured to receive spectral data and ambient light type information of a plurality of reference ambient light conditions, a comparison unit configured to compare the spectral data of the ambient light to be estimated with the spectral data of the plurality of reference ambient light conditions, and an estimation unit configured to estimate an ambient light type of the spectral data of the ambient light to be estimated from the ambient light type information of the reference ambient light based on a result of comparison provided by the comparison unit.