Inter-view local texture analysis based stereo image reversible data hiding

To enhance security of three-dimensional images, an inter-view local texture analysis (ILTA) based stereo image reversible data hiding method is presented. Due to low accuracy of existing predictors, two novel predictors are proposed to improve the prediction precision. In the first predictor, a texture analysis model is built by using ILTA, in which the texture similarity between a pair of matched pixels in the stereo image is used to classify pixels into horizontal texture, vertical texture, smooth and complex types. Thus, the accurate prediction is adaptively computed by considering the pixel type. Moreover, an intra-view based predictor as the second predictor is also described to predict pixels by optimal weights finding (OWF). Since ILTA and OWF predictors are combined to predict pixels in the stereo image, sharp prediction error histograms of two views are both constructed, and then multi-level histogram shifting is used to embed secret data reversibly for obtaining low image distortion and high embedding capacity. Experimental results demonstrates that ILTA and OWF predictors can obtain precise predicted values, and the proposed data hiding method outperforms some state-of-the-art data hiding methods in terms of embedding capacity and quality of stego stereo image.