Self-adaptive Error Modeling for Reversible Image Watermarking
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Solution Overview
Problem
Reversible image watermarking technologies struggle to completely remove non-linear correlation redundancy between pixels, limiting the embeddable watermarking capacity due to the linear nature of most prediction algorithms used.
Innovation Solution
An error modeling method for prediction context is introduced, which involves scanning an original image to establish an omnidirectional predictor context, self-adaptively modeling the prediction context to obtain a self-adaptive error model, and feeding output data from this model back to update and correct the prediction values, effectively addressing non-linear correlation redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear prediction algorithms are used to estimate current pixel values from prediction context, then linear correlation redundancy between pixels can be effectively removed, but non-linear correlation redundancy (such as texture redundancy) cannot be removed
Solution Approach 1:
The patent transforms the prediction error distribution by applying cumulative distribution function (CDF) mapping to convert non-uniform error distributions into uniform distributions. This parameter transformation enables the system to handle non-linear correlations effectively while maintaining prediction accuracy, resolving the contradiction between linear prediction precision and non-linear adaptability
Solution Approach 2:
The patent combines multiple prediction algorithms (linear prediction and non-linear prediction) to form a composite prediction system. By integrating different prediction strategies and selecting the most appropriate one based on local image characteristics, the system achieves both high prediction accuracy for linear correlations and adaptability for non-linear correlations such as textures
2Productivity
If simple prediction context estimation is used, then the processing is simple and fast, but the embeddable watermarking capacity is limited due to incomplete redundancy removal
Solution Approach 1:
The patent segments the prediction error distribution into multiple intervals based on the cumulative distribution function, and applies different transformation strategies to different segments. This segmentation approach enables more thorough redundancy removal to increase watermarking capacity, while the segmented structure keeps the computational complexity manageable by processing different regions with appropriate methods
Solution Approach 2:
The patent implements feedback mechanisms where prediction errors are analyzed and used to adaptively adjust the prediction context and parameters. This feedback loop continuously improves prediction accuracy and redundancy removal effectiveness, increasing embeddable watermarking capacity while the adaptive nature prevents excessive complexity growth
Data Source
AI summary
The present disclosure discloses an error modeling method and device for prediction context of reversible image watermarking. A predictor based on omnidirectional context is established; then, the prediction context is self-adaptively error modeled to obtain a self-adaptive error model; and finally, output data from the self-adaptive error model is fed back to the predictor to update and correct the prediction context, so as to correct a prediction value of a current pixel x[i,j]. Since the non-linear correlation between the current pixel and the prediction context thereof, i.e., the non-linear correlation redundancy between pixels can be found by the error modeling of the prediction context of the predictor, the non-linear correlation redundancy between the pixels can be effectively removed. Thus, the embeddable watermarking capacity can be increased.