A Novel Dual Predictors Framework of PEE

TL;DR

This paper introduces an improved 2D-PEH method using dual predictors with low correlation, enhancing embedding quality and speed, and extends PEE to a more general framework through comparative experiments.

Contribution

It presents a novel dual predictor framework for PEE that improves efficiency and quality, and extends the applicability of PEE methods.

Findings

Enhanced embedding quality compared to previous methods

Faster processing due to optimized predictor selection

Superior performance verified through comparative experiments

Abstract

In this paper, we propose a improved 2D-PEH based on double prediction-error. First,different from previous 2D-PEH, the proposed 2D-DPEH is established by selecting two distinct predictors with low correlation to calculate double prediction errors for each pixel. In addition, we adopt DP to optimize the selection of expansion bins, speeding up the running time and improving the quality of the embedded image. Finally,we combined the proposed method with C-PEE and original MHM, then designed comparative experiments with state of-the-art Pee-based methods in recent years to verify the superiority of the proposed algorithm and extend PEE into a more general case.