Robust watermarking based on blur‐guided JND model for macrophotography images

Abstract

Macrophotography Images (MPIs) have recently emerged as an active topic due to the development of mobile phone camera technology. A large number of MPIs have been rapidly increasing in many rich visual services, such as smartphones or high-definition monitors. MPIs are often composed of sharp macroimage and blur background, which exhibit different perceptual properties that often lead to different just noticeable difference (JND) estimation. Inspired by this, we formulate the blur concealment (BC) as another factor to determine the total masking effect: the interaction is relatively straightforward with a limited masking effect in the sharp regions, and is complicated with a strong masking effect in the blur parts. Furthermore, texture and orientation adaption and color information weighting are separately incorporated into the contrast masking and color masking. Finally, considering both BC and masking effects, a novel robust watermarking framework based on the proposed blur-guided JND model for MPIs, targeting at further improving the MPIs copyright protection performance. Extensive experiments on MP2020 and Blur Detection data sets show that the applicability of the proposed JND model in the scenario of perceptually MPIs watermarking, and our proposed scheme can outperform the state-of-the-art watermarking schemes by providing better robustness performance at the uniform visual quality.