Reversible Data Hiding in Encrypted Images with Extended Parametric Binary Tree Labeling

Abstract

Images uploaded to the cloud may be confidential or related to personal private information, so they need to be encrypted before uploading to the cloud storage. At the service provider side, appending additional information is usually required for transmission or database management. Reversible data hiding in encrypted images (RDHEI) serves as a technical solution. Recent RDHEI schemes successfully utilize the spatial correlation between image pixel values to vacate spare room for data hiding, however, the data payload can be further improved. This paper proposes a RDHEI scheme based on extended parameter binary tree labeling, which replaces non-reference pixel values with their prediction errors in a reduced length to vacate space. We further encode the prediction error of non-embeddable pixels to fit the space left from labeling. Thus, the space required to store the pixel bits replaced by labeling codes is saved. Experimental results show that the data payload of the extended parametric binary tree labeling outperforms state-of-the-art schemes. The embedding rates for the commonly applied datasets, including Bossbase, BOWS-2, and UCID, are 3.2305 bpp, 3.1619 bpp, and 2.8113 bpp, respectively.