Human visual system (HVS) is less sensitive to the high resolution detail bands like HL, LH and HH. Wavelet domain hides data in these regions. Hiding data in the high resolution regions increases the robustness while maintaining good visual quality. In discrete wavelet transform, the wavelet filters have floating point coefficients. When the data is hided in their coefficients, any truncations of the floating point values of the pixels that should be integers may cause the loss of the hidden information. It may lead to the failure of the data hiding system .
The problems of floating point precision of the wavelet filters is that when the input data is integer as in digital images, the output data will no longer be integer which doesn’t allow perfect reconstruction of the input image .
Integer wavelet transform maps an integer data set into another integer data set. In IWT, there will be no loss of information through forward and inverse transform . In the case of IWT, the LL sub band appears to be a close copy of the original image, with smaller scale, while in the case of DWT; the resulting LL sub band is distorted. Lifting schemes is used to perform integer wavelet transform. Lifting consists of three stages: split, predict and update ]