Flexible spatial location-based PVO predictor for high-fidelity reversible data hiding

Abstract

An advanced predictor offers the most effective way for performance enhancement of prediction-error expansion (PEE) based reversible data hiding. Unlike conventional predictors which exploit the correlation among adjacent pixels, pixel-value-ordering (PVO) exploits the one between the largest/smallest two pixels within block regardless of where they are located. Later, the spatial location received the attention from Peng et al. and led to better expansion bins selection. In this paper, a flexible spatial location (FSL) strategy is proposed to optimize the utilization of spatial correlation in PVO prediction. With FSL, pixels within block are no longer collected in a fixed way (e.g., from top to bottom, left to right). Specifically, eight modes of defining spatial location are designed for pixel collection. After pixel sorting, the inverse number of the location sequence is used to evaluate all modes and determine the best one. By determining the optimal mode for each block, the prediction becomes adaptive and thus a prediction-error histogram with higher expansion bin will be obtained. Numerous PVO-based schemes will benefit from FSL. In this paper, how FSL is applied to conventional PEE and high-dimensional PEE is also introduced. Experimental results demonstrate that FSL-PVO is of great significance to better capacity-distortion trade-off.