Image perceptual hashing has been proposed to identify or authenticate image contents in a robust way against distortions caused by compression, noise, common signal processing and geometrical modifications, while still holding a good discriminability for different ones in sense of human perception. We propose and compare four normalized block mean value based image perceptual hashing algorithms which demonstrate higher performances than other existing algorithms in robustness-anddiscriminalibility and simplicity for implementation. Overlapped blocking and rotation operations are employed to enhance the robustness to geometrical distortions. To evaluate the proposed algorithms? robustness and discriminability, given fixed modifications, identification ratio is used; and given fixed content classification, receiver operating curves is obtained.