PDF Artwork Signal Embedding for Robust Detection With Low Luminance Change
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Solution Overview
Problem
Existing technologies face challenges in encoding information signals in printed objects like product packaging and labels while maintaining low luminance changes and ensuring robustness, particularly in color artwork.
Innovation Solution
The method involves encoding information signals in color artwork by adjusting ink values to minimize luminance changes and using a blend model to optimize reflectance at specific wavelengths, such as 660 nm, while employing signal processing techniques to ensure robustness and detectability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If information signals are encoded in color artwork by adjusting ink values, then signal robustness is improved, but luminance change increases
Solution Approach 1:
The patent applies different encoding strategies to different color channels (Cyan, Magenta, Yellow, Black) based on their individual characteristics. By analyzing the local properties of each channel and applying targeted adjustments, the system achieves robust encoding while minimizing overall luminance changes through selective, localized modifications rather than uniform adjustments across all channels.
Solution Approach 2:
The system modifies ink value parameters (C, M, Y, K) within acceptable ranges to encode information signals. By carefully controlling the magnitude and direction of these parameter changes, the patent achieves signal embedding that maintains robustness while keeping luminance variations below perceptible thresholds, thus resolving the contradiction between reliability and illumination intensity.
2Measurement precision
If reflectance is optimized at specific wavelengths for signal detection, then detection precision is improved, but signal robustness under varying conditions deteriorates
Solution Approach 1:
The patent divides the signal encoding into multiple independent color channels (Cyan, Magenta, Yellow, Black), each optimized for detection at specific wavelengths. This segmentation allows the system to achieve high detection precision at targeted wavelengths while maintaining overall signal robustness through the distributed nature of the encoded information across multiple channels, so that no single wavelength optimization becomes a single point of failure.
Data Source
AI summary
The present disclosure relates generally to signal encoding for elements within PDF files. One implementation encodes an artwork element under different encoding conditions, and selects a winner version based on resulting signal robustness and/or visibility. Other implementations generate PDF layer masks to help determine overall embedding robustness, including interference from layered elements. Other implementations are provided too.