A Technique to Evaluate Upper Bounds on Performance of Pixel–prediction Based Reversible Watermarking Algorithms

Reversible watermarking algorithms allow distortion–free recovery of the cover image after watermark extraction. Current state–of–the–art does not allow the prediction of the upper bounds of the embedding capacity and distortion characteristics of reversible watermarking algorithms for a given image. In this work, we develop a statistical modelling technique to derive closed form expressions for upper bounds on these performance metrics of pixel–prediction based reversible watermarking algorithms, independent of the actual algorithm used. Comparison of the derived metrics and performance trends with those obtained from two recently reported reversible watermarking algorithms show that the developed model is accurate and consistent.

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