Minimum Rate Prediction and Optimized Histograms Modification for Reversible Data Hiding

PROJECT TITLE:

Minimum Rate Prediction and Optimized Histograms Modification for Reversible Data Hiding

ABSTRACT:

Prediction-error enlargement (PEE)-based reversible knowledge hiding schemes consist of two steps. 1st, a sharp prediction-error (PE) histogram is generated by utilizing pixel prediction methods. Second, secret messages are reversibly embedded into the prediction-errors through expanding and shifting the PE histogram. Previous PEE methods treat the two steps independently whereas they either specialise in pixel prediction to obtain a sharp PE histogram, or aim at histogram modification to reinforce the embedding performance for a given PE histogram. This paper propose a pixel prediction methodology primarily based on the minimum rate criterion for reversible knowledge hiding, that establishes the consistency between the two steps in essence. And correspondingly, a novel optimized histograms modification theme is presented to approximate the optimal embedding performance on the generated PE sequence. Experiments demonstrate that the proposed technique outperforms the previous state-of-art counterparts considerably in terms of each the prediction accuracy and the final embedding performance.

Did you like this research project?

To get this research project Guidelines, Training and Code… Click Here

COMMENTS :

Leave a Reply

Your email address will not be published. Required fields are marked *

12 ÷ three =