Image Quality Assessment Scheme Based on Structural Contrast Index and Gradient Similarity

Image quality assessment (Anmin et al. in Image Proc, IEEE Trans 21(4):1500–1512, 2012 [1]) is very important for image processing. A good image evaluation algorithm is consistent with subjective evaluations and has low computational complexity. A lot of image quality assessment methods have been proposed in recent years. Structural Contrast Index (SCI) has been proved can effectively reflect the complexity of image texture and model the masking effect of human visual system (HVS), so SCI is used as an important feature. HVS is very sensitive to edge region, however, SCI can’t correctly model the edge region structure. So the gradient similarity was incorporated into our method. An image quality assessment scheme based on structural contrast index and gradient similarity was proposed in our paper. Extensive experiments conducted on TID2013 image database demonstrate the performance this scheme is slightly better than the state-of-art methods not only on prediction accuracy but computational complexity.