Multi-Stage Light Source Sorting for Observer-Dependent Color Consistency
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Solution Overview
Problem
Conventional light source sorting methods fail to ensure consistent color perception across varying observer conditions, such as different observer sizes and ages, leading to potential color variations in lighting systems.
Innovation Solution
A multi-stage sorting method using multiple sets of spectral value functions to categorize light sources into color classes, ensuring they produce the same color impression regardless of observer variables, including field size, age, and ethnicity, by defining color loci in different color models and applying metameric indices for observer dependence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional single-set spectral value functions are used for sorting light sources, then sorting process is simple and fast, but color consistency across different observer conditions cannot be ensured
Solution Approach 1:
The sorting process is divided into multiple stages, each using a different set of spectral value functions corresponding to different observer conditions (e.g., different field sizes, ages). Light sources are sorted sequentially through these stages, with each stage refining the color consistency for specific observer groups. This segmentation allows comprehensive observer coverage while maintaining manageable process complexity at each individual stage.
Solution Approach 2:
The invention extends the sorting process from a single-dimension approach (one spectral value function set) to a multi-dimensional approach by incorporating multiple sets of spectral value functions that represent different observer dimensions (field size, age, ethnicity). This dimensional expansion ensures color consistency across diverse observer conditions without requiring an unmanageably complex single-stage system.
2Measurement precision
If multiple sets of spectral value functions are used to account for different observer conditions, then color perception consistency is improved, but measurement and sorting complexity increases
Solution Approach 1:
Multiple sets of spectral value functions are pre-calculated and prepared for different observer conditions (standard observer, young observer, elderly observer, different field sizes) before the sorting process begins. This preliminary preparation allows the actual sorting measurements to simply compare against pre-established criteria, reducing real-time measurement complexity while maintaining high precision across different observer conditions.
Solution Approach 2:
The invention systematically varies key parameters of the spectral value functions (observer field size, observer age, ethnicity) to create multiple function sets. By controlling and standardizing these parameter variations, the measurement process becomes more structured and manageable, allowing precise color perception assessment across different observer conditions without overwhelming complexity.
3Manufacturing precision
If light sources are sorted using a single color model, then sorting is straightforward, but color uniformity under varying observation conditions deteriorates
Solution Approach 1:
The sorting system is designed to be universal by incorporating multiple color models (e.g., CIE 1931, CIE 1964, CIE 2015) that can handle different observer conditions. Each color model serves a specific function for particular observer types, but collectively they provide universal coverage across all observer conditions. This multi-functionality allows a single sorting system to achieve both precision and adaptability.
Solution Approach 2:
The invention creates a composite sorting approach by combining multiple color models and spectral value function sets into a unified sorting framework. Just as composite materials combine different materials to achieve superior properties, this composite sorting methodology combines multiple color models to achieve both precise sorting and adaptability to varying observer conditions, overcoming the limitations of any single color model.
Data Source
Figure 1a~1c
Figure 2
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AI summary
In a method for sorting light sources (1 ad) into a color class (FC), in a first step (100) a coarse selection set (CSP) is sorted from a basic set (SSP) of light sources (1 ad) into an intermediate class (IC), wherein the first color loci of the light sources (1 a-d) of the intermediate class (IC) are arranged in a common first region around a first reference color locus, wherein the first reference color locus and the first color loci are defined in a first color model with a first set of spectral value functions (CMF1), in a subsequent second step (200) a fine selection set (FSP) is sorted from the coarse selection set (CSP) of light sources (1 a-d) into the color class (FC), wherein the second color loci of the light sources (1 ad) of the color class (FC) are arranged in a common second region around a second reference color locus.wherein the second reference color locus and the second color loci are defined in a second color model with a second set of spectral value functions (CMF2), the first (CMF1) and the second set of spectral value functions (CMF2) being differently designed.