Nonlinear additive model based saliency map weighting strategy for image quality assessment

Most state-of-the-art image quality metrics are based on the two-step approach: local distortion/fidelity measurement and pooling. During the pooling stage, many weighting strategies have been proposed incorporating properties of the distortion itself, various masking effects and visual attention. Recently, researchers have devoted great enthusiasm and effort to the improvement of image quality assessment using visual saliency models. In this research, it is noticed that visual saliency features of both the original image and the distorted one have impacts on the process of image quality assessment. To reduce the overlapping effects, a nonlinear additive model is proposed to integrate saliency features from the original and distorted images towards improved error weighting results. Our extensive experimental studies on four publicly available image databases (LIVE, TID2008, CSIQ and A57) indicate that the proposed improved nonlinear additive model based saliency map weighting strategy constantly leads to higher prediction accuracy for image quality assessment than traditional methods.