基于图像块分类阈值优化的改进可逆图像伪装

doi:10.3969/j.issn.0255-8297.2018.02.003

Abstract

Abstract:

In order to improve the visual quality of stego images in digital image camouflage, a method of reversible image camouflage based on the threshold optimization of image sub-block classifcation is proposed. First, the sub-blocks of the original image and the cover image are classifed, respectively, according to their statistical characteristics. The threshold for classifcation is optimized through minimizing the mean square error of the camouflage image and cover image. Then, after the processes of the image sub-block matching, image sub-block linear transformation, sub-block rotation and horizontal flipping, a stego image which is visually similar to the cover image is generated. The transformation parameter information used for restoring the original image is eventually embedded into the stego image in a reversible manner to generate the fnal camouflage image. Therefore, the receiver side can extract the auxiliary information to realize the lossless recovery of the original image. The experimental results show that the visual quality of the camouflage image generated by the proposed method is better than that of the image without classifcation threshold optimization.

Key words: threshold optimization, image camouflage, block classifcation, reversible data hiding

CLC Number:

TP317.4

LIU Xiao-kai, YAO Heng, QIN Chuan. Improved Reversible Image Camouflage Method Based on Image Block Classifcation Threshold Optimization[J]. Journal of Applied Sciences, 2018, 36(2): 237-246.

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Improved Reversible Image Camouflage Method Based on Image Block Classifcation Threshold Optimization

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