非因果预测和直方图平移下的可逆音频数字水印

Reversible Audio Watermarking Based on Non-causal Prediction and Histogram Shifting

预测算法在可逆水印隐藏中扮演重要角色.预测算法越好,得到的预测值就越精确,嵌入水印后的失真度就越低.为此,提出一种新的非因果音频预测算法.在预测之前,利用每相邻3个样本点之间的相关性计算出一个预测系数,再将这些预测系数进行统计平均得到最佳预测系数.根据每相邻3个样本点间的数值关系计算预测值和非整数预测误差.最后利用直方图平移技术嵌入水印信息.该算法利用了相邻3个样本点间的相关性,而且集合了非因果预测的优异性和音频信号本身的固有特性.测试了6个标准的音频文件,对比了已有同类可逆音频水印算法,该算法在预测性能和嵌入失真方面都更有优势.

Prediction algorithms play an important role in reversible audio watermarking.This paper presents a novel prediction algorithm for reversible watermarking.A prediction parameter is obtained from relations among each group of three consecutive samples,and an optimum prediction coefficient is then calculated by averaging all these parameters in a clip.The prediction value of the current sample and the corresponding prediction error are computed.Since the prediction error is non-integer,we use an improved histogram shifting method to embed the additional data.The proposed scheme can better explore audio samples＇ correlations by using non-causal prediction and computing prediction parameter in a statistical way.We demonstrate validity of the scheme with a set of six standard audio files.Compared with three state-of-art schemes,the proposed scheme has lower embedding distortion with the same payload.