基于图像块分类阈值优化的改进可逆图像伪装

摘要

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 classification is proposed.First,the sub-blocks of the original image and the cover image are classified,respectively,according to their statistical characteristics.The threshold for classification 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 final 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 classification threshold optimization.%为了提高数字图像伪装中生成的伪装图像的视觉质量,提出一种基于图像块分类阈值优化的可逆图像伪装方案.根据统计特征对原始图像子块和目标图像子块进行分类,通过优化分类阈值使得生成伪装图像和目标图像的均方差最小,然后进行相应类内图像块匹配、块线性变换、块旋转和翻转校正,最终使原始图像伪装成目标图像.采用可逆信息隐藏的算法将恢复原图所需的变换参数信息嵌入伪装图像,接收方提取辅助信息,从而实现原始图像的无损恢复.实验结果表明,该算法生成的伪装图像的视觉效果比未经阈值优化的伪装图像的效果更好.