A Video-Driven Just Noticeable Distortion Profile for Watermarking

Perceptual watermarking for video needs to take full advantage of the results of human visual system (HVS) studies. Just noticeable distortion (JND), which refers to the maximum distortion that the HVS does not perceive, gives us a way to model the HVS accurately. Since motion is a specific feature of video, estimation of the JND profile for video needs to take into account the temporal HVS properties in addition to the spatial properties. In this paper, we develop a video-driven JND profile for watermarking which incorporates the temporal modulation factor, retinal velocity, luminance adaptation and block classification. Experimental results with subjective test confirm the improved performance of our video-driven JND profile for watermarking. Our video-driven JND profile, which responds to motion correctly is capable of yielding higher injected-watermark energy without introducing noticeable distortion to the original video sequences and outperforms the relevant existing models.