Adaptively imperceptible video watermarking based on the local motion entropy

This paper proposes an adaptively imperceptible video watermarking algorithm using the entropy model for local motion characterization. The algorithm firstly combines Human Visual System (HVS) with the block- matching techniques to get the motion-related information. Then it utilizes the entropy model to statistically analyze above motion-related information to obtain the motion entropy of frame. Successively, this algorithm divides every frame into local regions, and then local motion entropy can be obtained according to the motion-related information in local region. By combining the local motion entropy with the motion entropy of frame, the motion characteristics visual masking is adaptively calculated. Based on the motion characteristics visual masking and the contents of video frames, the maximum strength of watermarking is calculated in every block. Experiments indicate that using entropy to local motion characteristics can significantly improve the watermarking imperceptibility, effectively resist common attacks for video watermarking and consequently achieve higher robustness.

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