Reversible data hiding (RDH) for JPEG images remains relatively underexplored, with key challenges lying in coefficient selection and modification strategies. Existing methods select coefficients for embedding through block-wise or frequency-band-based operations, resulting in coarse-grained decisions that constrain embedding performance. In this paper, a novel RDH scheme for JPEG images based on gap-driven histograms generation with coefficient-wise selection is proposed. First, a multi-metric weighted complexity and coefficient-wise selection approach is proposed, integrating four local feature criteria to assess each coefficient individually, enabling more precise per-coefficient selection. Then, a gap-driven adaptive multi-histogram generation strategy is introduced, leveraging gap pairs to minimize shifting distortion by segmenting histograms via bisection and avoiding modifications to high-magnitude coefficients. Experimental results confirm that the proposed method achieves improved visual quality and more efficient file size control compared to existing state-of-the-art approaches.

As a specific data hiding technique, reversible data hiding (RDH) has recently received extensive attention. By this technique, both the embedded data and the original cover image can be exactly extracted from the marked image. In our previous work, a general expansion-shifting model for RDH is proposed by introducing the so-called reversible embedding function (REF). With REF, RDH can be designed and the corresponding rate-distortion formulations can be established, providing an approach to optimize the reversible embedding performance. In this paper, by extending our previous work, optimal REF for one-dimensional histogram is investigated, and all optimal REF are derived in this case when the maximum modification to the cover pixel is limited as a small value. Moreover, based on the derived optimal REF for one-dimensional histogram and multiple histograms modification, a practical RDH scheme is presented and it is experimental verified better than some state-of-the-art algorithms in terms of capacity-distortion performance.