The edge information of images is important in image quality assessment. Nonetheless

the image quality evaluation method of feature similarity (FSIM) based on low-level features is not ideal for edge information detection although this algorithm considers the significance of low-level features. On the basis of the information provided above

this study combines the FSIM algorithm with the grid scheduling simulator (GSSIM) algorithm

which is sensitive to edge information

to generate the new image quality assessment method FGSIM. The new method is not only consistent with the characteristics of human visual systems

but it can also identify image edges effectively.The algorithm combines the part of FSIM that represents phase consistency with the component of GSSIM algorithm that can extract image information to generate the new image-quality assessment method FGSIM. The use of phase congruency represents image features

the part of phase consistency that can be used to keep the algorithm close to the human visual system

and the part of the GSSIM algorithm that can extract image information realized by Gradient. This part can be employed to identify image edges.FSIM

GSSIM

and FGSIM algorithms were used to evaluate images containing different motion blurs

and graphs were constructed to represent the obtained data. In the motion blur experiments

the numerical value of the FGSIM algorithm declines from 0.8943 to 0.3443 with the increase in image blur. Changes are significant

and the motion blur is highly sensitive. In the Gaussian blur and Gaussian noise experiments

the changes in the numerical degree value of the FGSIM algorithm are superior to those in the FSIM algorithm to some extent

although the former is inferior to the GSSIM algorithm. Experimental results on public image quality databases show that the scatter diagram of the FGSIM algorithm is slightly inferior to that of the FSIM algorithm. However

the former is significantly better than that of the GSSIM algorithm. Furthermore

the scatter diagram of the FGSIM algorithm is more concentrated than that of the GSSIM algorithm. The FGSIM algorithm also performs better than the GSSIM algorithm in terms of Pearson correlation coefficient

Spearman rank-order correlation coefficient

Kendall rank-order correlation coefficient

and root mean square error. These factors are commonly used to measure performance.Experimental results indicate that the FGSIM algorithm is a new image-quality assessment method that is not only consistent with human visual system characteristics but can also identify image edges effectively. Thus

this algorithm can identify edge information well and is sensitive to variations in image quality.