Histogram Matching-Based Reversible Data Hiding for Intelligent Transportation Applications

In today’s intelligent transportation systems, the effectiveness of image-based analysis relies heavily on image quality. To enhance images while preserving reversibility, this article proposes a histogram matching-based reversible data hiding (HMRDH) method. The proposed approach ensures high-embedding capacity by incorporating histogram shifting from reversible data hiding with contrast enhancement (RDHCE). Unlike traditional RDHCE methods that are limited to achieving histogram equalization, our method constrains the sequence of bin adjustments to achieve flexible histogram matching. The algorithm first evaluates bin heights to assign roles, then iteratively selects and adjusts corresponding bins while embedding information. To prevent deviation from the target histogram, this method calculates the root mean square error after each iteration to ensure that the adjustment is retained only if it improves the matching accuracy. When necessary, the original image and embedded information can be fully recovered. Extensive experiments demonstrate the superiority of the proposed method in visual quality, embedding capacity, and adaptability.