Adaptive spread transform QIM watermarking algorithm based on improved perceptual models

The quantization step is one of the most important factors which affect the performance of quantization watermarking used for image copyright protection. According to the characteristic of perceptual model and the specific attacks, improved perceptual model and different implementations of perceptual model are proposed. They are incorporated into the spread transform quantization index modulation (ST-QIM) framework. The experimental results show that the four algorithms we proposed in this paper can reduce the noise attacks and facilitate common digital image processing operations. Among these, adaptive ST-QIM based on further modified Watson model (ST-QIM-fMW-SS) and adaptive ST-QIM based on modified sensitivity model (ST-QIM-MS-SS) have better performance.