Reversible data hiding by adaptive group modification on histogram of prediction errors

In this work, the conventional histogram shifting (HS) based reversible data hiding (RDH) methods are first analyzed and discussed. Then, a novel HS based RDH method is put forward by using the proposed Adaptive Group Modification (AGM) on the histogram of prediction errors. Specifically, in the proposed AGM method, multiple bins are vacated based on their magnitudes and frequencies of occurrences by employing an adaptive strategy. The design goals are to maximize hiding elements while minimizing shifting and modification elements to maintain image high quality by giving priority to the histogram bins utilized for hiding. Furthermore, instead of hiding only one bit at a time, the payload is decomposed into segments and each segment is hidden by modifying a triplet of prediction errors to suppress distortion. Experimental results show that the proposed AGM technique outperforms the current state-of-the-art HS based RDH methods. As a representative result, the proposed method achieves an improvement of 4.30dB in terms of PSNR when 105,000 bits are hidden into the test Lenna image.