收稿日期: 2018-02-01
网络出版日期: 2018-03-31
基金资助
国家自然科学基金（No.61702332，No.61672354）；广西多源信息挖掘与安全重点实验室开放基金（No.MIMS16-03）资助
Improved Reversible Image Camouflage Method Based on Image Block Classifcation Threshold Optimization
Received date: 2018-02-01
Online published: 2018-03-31
刘小凯, 姚恒, 秦川 . 基于图像块分类阈值优化的改进可逆图像伪装[J]. 应用科学学报, 2018 , 36(2) : 237 -246 . DOI: 10.3969/j.issn.0255-8297.2018.02.003
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.
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