Sorting Methods and Adaptive Thresholding for Histogram Based Reversible Data Hiding

TL;DR

This paper introduces a novel histogram-based reversible data hiding scheme that uses advanced pixel sorting techniques and hiding intensity analysis to improve embedding capacity and image quality compared to existing methods.

Contribution

It proposes two new sorting methods combined with hiding intensity analysis to enhance data embedding efficiency and image quality in reversible data hiding.

Findings

Higher hiding capacity at fixed distortion levels.

Improved image quality in marked images.

Outperforms state-of-the-art RDH algorithms.

Abstract

This paper presents a histogram based reversible data hiding (RDH) scheme, which divides image pixels into different cell frequency bands to sort them for data embedding. Data hiding is more efficient in lower cell frequency bands because it provides more accurate prediction. Using pixel existence probability in some pixels of ultra-low cell frequency band, another sorting is performed. Employing these two novel sorting methods in combination with the hiding intensity analysis that determines optimum prediction error, we improve the quality of the marked image especially for low embedding capacities. In effect, comparing to existent RDH algorithms, the hiding capacity is increased for a specific level of the distortion for the marked image. Experimental results confirm that the proposed algorithm outperforms state of the art ones.