Textures and reversible watermarking

This paper investigates the effectiveness of prediction-error expansion reversible watermarking on textured images. Five well performing reversible watermarking schemes are considered, namely the schemes based on the rhombus average, the adaptive rhombus predictor, the full context predictor as a weighted average between the rhombus and the four diagonal neighbors, the global least-squares predictor and its recently proposed local counterpart. The textured images are analyzed and the optimal prediction scheme for each texture type is determined. The local least-squares prediction based scheme provides the best overall results.