EFFICIENT ADAPTIVE PREDICTION BASED REVERSIBLE IMAGEWATERMARKING

In this paper, we propose a new reversible watermarking algo-rithm based on additive prediction-error expansion which can recover original image after extracting the hidden data. Em-bedding capacity of such algorithms depend on the prediction accuracy of the predictor. We observed that the performance of a predictor based on full context prediction is preciser as compared to that of partial context prediction. In view of this observation, we propose an efficient adaptive prediction (EAP) method based on full context, that exploits local char-acteristics of neighboring pixels much effectively than other prediction methods reported in literature. Experimental re-sults demonstrate that the proposed algorithm has a better em-bedding capacity and also gives better Peak Signal to Noise Ratio (PSNR) as compared to state-of-the-art reversible wa-termarking schemes. Index Terms — Reversible image watermarking, adaptive prediction, embedding capacity