Sorting Methods and Adaptive Thresholding for Histogram Based Reversible Data Hiding
This work addresses data hiding efficiency in images, particularly for low embedding capacities, but appears incremental as it builds on existing histogram-based RDH schemes.
The paper tackles the problem of improving image quality in reversible data hiding by introducing two novel sorting methods and adaptive thresholding, resulting in increased hiding capacity for a given distortion level compared to existing algorithms.
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.