Emulating Spectral Measurement via Predictive Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Spectral measurement devices used in color rendering devices are limited in availability, making it challenging to accurately measure colors with increased demands, especially when they are fixed in specification and cannot detect certain spectral features like those outside the visible range or require different measurement variables.
Innovation Solution
A predictive model is trained using parameter values generated from measurements by both primary and ancillary spectral measurement devices to emulate the output of the ancillary device, allowing the primary device to predict spectral characteristics beyond its measurement capabilities, such as a wider range of wavelengths or different variables, using an emulator integrated into the color measurement apparatus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a primary spectral measurement device is used, then device complexity is reduced, but measurement precision is insufficient for certain spectral features
Solution Approach 1:
The patent creates a virtual copy of the ancillary spectral measurement device through an emulator that generates predicted spectral measurements. This emulator replicates the functionality of the more complex ancillary device without requiring its physical presence, allowing the primary device to access spectral information that would otherwise be unavailable while avoiding the added hardware complexity.
Solution Approach 2:
The emulator acts as an intermediary between the primary spectral measurement device and the ancillary device's measurement capabilities. It processes data from the primary device and translates it into predicted spectral measurements that mimic the ancillary device's output, enabling extended measurement precision without direct integration of the complex ancillary hardware.
2Measurement precision
If ancillary spectral measurement devices are added to extend measurement range, then measurement precision improves, but device complexity increases
Solution Approach 1:
Instead of physically adding ancillary spectral measurement devices to extend the measurement range, the patent creates virtual copies through an emulator. This emulator generates predicted spectral measurements for wavelengths and variables that the primary device cannot measure, effectively extending the measurement range without the physical complexity of additional hardware.
Solution Approach 2:
The emulator provides multi-functionality by enabling the primary device to access multiple types of spectral measurements (visible and invisible wavelengths, different measurement variables) through a single integrated system. This universal approach allows one device to perform functions that would traditionally require multiple specialized devices.
3Measurement precision
If multiple spectral measurement devices are deployed, then measurement precision improves, but ease of operation deteriorates
Solution Approach 1:
The patent merges the functionality of multiple spectral measurement devices into a single integrated system. By combining the primary device with the emulator that virtualizes ancillary device capabilities, the system provides comprehensive spectral measurement and color calibration functions through one unified interface, eliminating the operational complexity of managing multiple separate devices.
Data Source
AI summary
Certain examples relate to emulating a spectral measurement device in a color measurement apparatus. In these examples, a primary spectral measurement device measures a first spectral characteristic of a rendered color output. A predictive model, parametrized by parameter values, is applied to the measurement from the primary spectral measurement device to determine a predicted measurement of a second spectral characteristic of the rendered color output which would be measured by an ancillary spectral measurement device. Parameter values are generated by training the predictive model with data from the primary spectral measurement device and the ancillary spectral measurement device.