基于插值预测和块分类的低失真可逆数据隐藏

Reversible data hiding with low distortion based on interpolation prediction and dividing blocks model

提出一种基于插值预测和块分类模型的低失真可逆数据隐藏算法。首先,对原始图像进行插值预测,得到预测误差图像;然后根据误差图像建立直方图,使得产生的直方图更陡峭,峰值点出现频率更大;接着对误差图像进行分块,利用块分类模型,在隐藏块内利用直方图平移隐藏信息,尽可能地减少平移点的数量;最后,相应地修改原始像素的灰度值,得到低失真的隐秘图像。实验结果表明,本文算法不仅可以实现可逆数据隐藏,而且隐藏效果较好,有效载荷较高。

To reduce the distortion o{ stegcrimage,a reversible data mcung scnelne L~ tJ^ut^u~u interpolation prediction and dividing blocks model. At first,the original image is processed to get the in terpolation-error image using the interpolation technique which is the process of producing a high-resolu tion image from its lower-resolution counterpart. The interpolation-error equals the difference between pixel value and its interpolation value. Then the interpolation-error histogram can be built. Compared with other histograms, this interpolation-error histogram will be more steak and the chance to obtain peak points will be higher. Next, the interpolation- error image will be divided into blocks, and by using the model, the blocks with peak points will be identified, which are the only candidates to hide secret data based on the predictio^error histogram shifting. So the number of shifted points will be decreased during the hiding. Finally, the stego-image is obtained by changing pixel values in the original image. The experi- ments show that this algorithm can restore the original image without any distortion after the hidden da- ta has 13een extracted. Furthermore,the quality of stegc^image is better and the embedding capacity is higher. The algorithm can be deployed for information hiding and image authentication.