Adaptive Digital Watermark Embedding via Local Luminance Analysis
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Solution Overview
Problem
Existing digital watermarking techniques require lengthy and complex calculations for embedding watermark information, and manually preset strategies often neglect the content characteristics of images or videos, making the embedded watermarks susceptible to removal.
Innovation Solution
A method that involves acquiring a second target image with low-luminance pixels from the original image, selecting candidate image areas using a sliding window, and determining the target embedding position based on comprehensive measurements of image texture and luminance information, thereby reducing computational work and enhancing embedding strategy adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a global pixel domain or frequency domain is selected for watermark embedding, then the embedding position can be determined, but lengthy and complex adjustment calculations are required that do not lend themselves to batch processing
Solution Approach 1:
The patent divides the image into multiple local areas and uses a sliding window to segment the search space for watermark embedding positions. This segmentation allows the system to evaluate multiple candidate positions in parallel without requiring global adjustment calculations, thereby enabling batch processing while maintaining precise embedding position determination.
2Productivity
If a manually preset local embedding strategy is used to select embedding position, then computational work is decreased, but the strategy neglects content characteristics of images or video frames making watermarks susceptible to removal
Solution Approach 1:
The patent applies local quality by evaluating content characteristics (such as texture complexity and luminance) at each local area of the image using a sliding window. This allows the system to adaptively select embedding positions based on local content properties rather than using a uniform manual strategy, thereby maintaining both computational efficiency and watermark protection strength.
Solution Approach 2:
The patent introduces a dynamic embedding position selection mechanism that adapts to local content characteristics. The sliding window dynamically evaluates different regions based on their texture and luminance properties, allowing the system to automatically adjust embedding positions according to content complexity rather than following a fixed manual pattern, thus preventing easy detection and removal.
3Reliability
If adaptive embedding position selection based on content characteristics is implemented, then watermark protection is enhanced, but computational complexity increases
Solution Approach 1:
The patent applies partial action by evaluating only the most relevant local areas of the image using a sliding window with predefined step sizes. Instead of analyzing the entire image globally, the system focuses computational resources on candidate regions that meet specific criteria, thereby reducing overall computational complexity while still achieving adaptive embedding position selection based on content characteristics.
Data Source
AI summary
Data processing is disclosed including acquiring a first target image to be embedded with information and to-be-embedded information, acquiring, using the first target image, a second target image corresponding to the first target image, the second target image corresponding to an image including low-luminance pixels in the first target image, the low-luminance pixels being pixels having a luminance no higher than a luminance threshold value in the first target image, selecting candidate image areas from the second target image, determining a target embedding position for the to-be-embedded information in the first target image based on the candidate image areas, and embedding the to-be-embedded information in the target embedding position in the first target image.