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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 530-531Randomness Evaluation for Scrambled Image Using...

Randomness Evaluation for Scrambled Image Using Intersecting Cortical Model Neural Network

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Abstract:

A bio-inspired visual neural network named ICMNN was adopted to extract image structural information for image scrambling evaluation. In order to describe image structure more effectively with ICMNN, image bitmap was introduced into ICMNN input field. First, the original image was decomposed into eight binary images and each was scrambled with Arnold transformation without loss of generality. Then, ICMNN was adopted to extract the structural feature sequence of bitmap images and their corresponding scrambled ones. Last, L1 norm of the structure change sequence between them was calculated to evaluate the scrambling degree of the scrambling images. Results show that combining image bitmap decomposition with ICMNN can effectively evaluate image scrambling degree and describe the change of the structure information, which agrees with human visual perception. This evaluation algorithm is also independent of the scrambling algorithm and has a good versatility.

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Advances in Measurements and Information Technologies

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Periodical:

Applied Mechanics and Materials (Volumes 530-531)

Pages:

434-442

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.530-531.434

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Cite this paper

Online since:

February 2014

Authors:

Jing Jing Zhao*, Guang Zhu Xu, Bang Jun Lei, Chun Lin Li

Keywords:

Arnold Transformation, Intersecting Cortical Model Neural Network (ICMNN), Structural Feature Sequence

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* - Corresponding Author

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