基于熵模型的高透明性自适应视频水印算法

提出一种基于视频运动估计熵模型的自适应视频水印算法.该算法将人类视觉系统(human visual system,简称HVS)与视频分块运动估计(block motion estimation of video)相结合,获取视频序列帧中与运动相关的视频运动信息,然后利用熵模型对视频序列帧中的运动信息进行统计,从而得到一组基于视频序列帧间运动信息与人类视觉屏蔽特性相结合的非线性计算公式.利用该组计算公式,可以根据视频帧的内容自适应地计算每个方块的水印最大嵌入强度.实验结果表明,熵模型与非线性公式的引入较大幅度地提高了视频水印的透明性,并且能够有效地抵抗常见的针对视频水印的攻击,具有较高的安全性和鲁棒性.

Adaptively Imperceptible Video Watermarking Algorithm Using Entropy Model

An adaptively imperceptible video watermarking algorithm using entropy model is proposed. This algorithm works by first combining the Human Visual System (HVS) with block-matching techniques to obtain motion-related information. It then uses entropy model to statistically analyze the obtained information, and eventually establish a set of non-linear formulas based on HVS and motion information. Based on the contents of video frames, this set of non-linear formulas can adaptively calculate the maximum strength of every block. Experimental results show that using entropy model and non-linear formulas can significantly improve watermarking imperceptibility, effectively resist common attacks for video watermarking, and consequently achieve higher robustness.