Image quality assessment (IQA)aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural-similarity (SSIM)index brings IQA from pixel-based stage to structure-based stage

and a multi-scale information content weighting approach based upon a Gaussian scale mixture(GSM)model of natural images

namely information content weighted SSIM image quality assessment algorithm (IW-SSIM). It was proposed recently and achieved significant and consistent performance improvement compared to SSIM-based IQA algorithms. The success of the IW-SSIM approach may be understood as a natural consequence of an effective combination of several approaches that have been proven useful in IQA research. These include multi-scale image decomposition followed by scale-variant weighting

SSIM-based local quality measurement

and information theoretic analysis of visual information content and fidelity. Aiming at improving the local distortion measurement of the IW-SSIM

we propose a novel algorithm called information content weighted gradient salience SSIM (IW-GS-SSIM). It is well known that human vision has a nonlinear perception to different physical stimuli (e.g. luminance)

which has been theorized and empirically proven by the psychologist E.H. Weber. Weber's Law is computed empirically but its effectiveness had been proven for several application fields. We assume that Weber's law is also suitable for image gradient magnitude

which has been empirically verified by testing subject-rated IQA databases. Because the human visual system (HVS)responds to the brightness stimulus mainly complying with Weber's law

the proposed algorithm only performs one pass filtering to calculate the contrast between the current pixel and its background quickly

which is used as a dimensionless measure of the visual significance of the gradient structure after applying nonlinear mapping. The gradient magnitude combined with Weber visual significance serves basis for characterizing the local image quality. Weber visual significance and the gradient magnitude play complementary roles in characterizing the image local quality

which is used to replace the SSIM local distortion metric of the IW-SSIM algorithm. Extensive experiments performed on six benchmark IQA databases demonstrate that as a local distortion metric the GS-SSIM has higher evaluation accuracy than SSIM for image noise and blurring distortion. Our extensive tests across six publicly-available independent image databases verified that IW-GS-SSIM's performances are often superior otherwise similar when compared to the other representative or prominent IQA algorithms in terms of correlation between objective measured quality values and subjective observations

validating that it is a very robust IQA algorithm. In summary

we propose a novel gradient magnitude saliency-based IQA algorithm in this paper

namely information content weighted gradient salience SSIM (IW-GS-SSIM). The underlying principle of the IW-GS-SSIM is that the HVS perceives an image mainly based on its salient low-level image feature. Specially

the perception nonlinearity of image gradient magnitude-based local image quality measurement is implemented

i.e. GS

which can better characterize image local quality measure. We believe that our results support the general principle underlying our approach

i.e.

some HVS properties on image gradient should been explored and incorporated into designing the new IQA algorithm. We expect that our works could give new insights to people who are interested in image quality assessment.