Multi-Dimensional Histogram Modification Framework for Reversible Data Hiding in JPEG Images

Reversible data hiding in JPEG images is critical for secure multimedia applications, while, existing histogram modification schemes are confined to fixed 1D or 2D dimensions. These methods fail to adapt to the characteristics of cover images and the demands of embedded data, resulting in suboptimal trade-offs among embedding capacity, image quality, and file size. To address these issues, this study proposes a Multi-Dimensional Histogram Modification Framework that advances histograms from "fixed dimension" to "variable dimension" and extends them to high-dimensional structures. Our key contributions are as follows: first, we propose an adaptive dimension selection strategy that dynamically determines the optimal histogram dimension by evaluating the capacity and distortion for each cover image and embedding task; second, we develop a high-dimensional histogram construction method that combines AC coefficients with absolute values of 1 and 2 at the same frequency to enhance the utilization of coefficient correlation; third, we design an optimized mapping algorithm that searches for the optimal mapping matrix to minimize both distortion and file expansion. Experimental results show that the proposed method outperforms existing 1D and 2D histogram modification schemes. On average, it increases the embedding capacity by 5000 bits, improves the image quality by 0.2 dB, and reduces the file size by 4000 bits.