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Multiscale relevance of natural images
Samy Lakhal1,2,3, Alexandre Darmon4, Iacopo Mastromatteo1,5
1Chair of Econophysics and Complex Systems, Ecole Polytechnique, 91128, Palaiseau Cedex, France.
Scientific Reports
|September 9, 2023
Summary
We developed a new Multiscale Relevance (MSR) measure to analyze image statistical properties. This method is more robust and informative than traditional techniques for image analysis and processing.
Area of Science:
- Information theory
- Statistical image analysis
- Computer vision
Background:
- Understanding statistical properties of natural images is crucial for image processing.
- Classical methods like power spectrum analysis have limitations in capturing image content robustness.
Purpose of the Study:
- Introduce and validate the Multiscale Relevance (MSR) measure.
- Assess image robustness to compression across multiple scales.
- Compare MSR with classical image analysis techniques.
Main Methods:
- Developed an agnostic information-theoretic approach.
- Defined the Multiscale Relevance (MSR) measure.
- Characterized MSR for synthetic textures and natural images.
Main Results:
- MSR effectively assesses image robustness to compression at all scales.
- Natural images exhibit similarities to critical random textures.
- MSR outperforms power spectrum analysis in robustness and information content.
Conclusions:
- The MSR approach offers advanced capabilities for image analysis and processing.
- MSR provides a high level of physical interpretability.
- This method is suitable for calibrating procedures like color mapping and denoising.
