Encoded Signal Robustness via Ink Spectral Reflectance Modeling
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Solution Overview
Problem
Current technologies face challenges in accurately predicting how print colors interact and blend when overprinted on various substrates, leading to poor encoded signal robustness and visibility in digital watermarking for product packaging, resulting in aesthetically subpar designs that may not detect well.
Innovation Solution
An image processing system that includes electronic processors to generate heatmaps for predicted signal detectability and swipe probabilities, using Kubelka-Munk and Yule-Nielsen models to estimate spectral reflectance of ink overprints, and adapting designs based on substrate opacity and geometry to ensure robust and visible encoded signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital watermarking is applied to product packaging, then encoded signal robustness and visibility are improved, but accurate prediction of ink blend interactions becomes difficult leading to subpar design quality
Solution Approach 1:
The system performs preliminary prediction of ink blend interactions and encoded signal detectability during the design stage using Kubelka-Munk and Yule-Nielsen models. This allows designers to identify and correct potential robustness issues before actual printing, preventing subpar design quality while maintaining signal reliability.
Solution Approach 2:
The patent replaces physical trial printing and manual assessment with computational models (Kubelka-Munk, Yule-Nielsen) that simulate ink blend behavior and signal detectability. This substitution enables accurate prediction without physical prototypes, resolving the contradiction between reliability improvement and measurement precision.
2Measurement precision
If ink overprint prediction is improved, then encoded signal detectability is enhanced, but design complexity and computational requirements increase
Solution Approach 1:
The prediction system performs multiple functions simultaneously: it models ink blend interactions using Kubelka-Munk theory, applies Yule-Nielsen corrections for overprint effects, and evaluates encoded signal detectability all in one integrated workflow. This multi-functionality reduces overall system complexity despite the advanced capabilities provided.
3Ease of operation
If subjective assessment methods are used for encoded signal quality, then design process is simpler, but objective grading and consistent quality control become difficult
Solution Approach 1:
The system replaces subjective human assessment with automated computational evaluation that calculates objective detectability metrics and generates quality grades. This substitution maintains design process simplicity while ensuring consistent, reliable quality control across all designs through standardized algorithms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system improves encoded signal prediction and visibility, ensuring robust detection and aesthetically pleasing designs by accurately modeling ink interactions and substrate effects, enhancing the reliability of digital watermarking in product packaging.
Implementation Method 1
Kubelka-Munk and Yule-Nielsen models to estimate spectral reflectance of ink overprints
Implementation Method 2
Kubelka-Munk and Yule-Nielsen models to estimate spectral reflectance of ink overprints
Implementation Method 3
Kubelka-Munk and Yule-Nielsen models to estimate spectral reflectance of ink overprints
Data Source
AI summary
The present disclosure relates generally to image signal processing, including encoding signals for image data or artwork. An aggregation module predicts likely detection of an encoded signal, including modeling detection in an environment in which an encoded signal is swiped in front of a camera system comprising at least two cameras.