Image quality is mainly affected by its structure and content. Traditional image quality evaluation metrics based on structural similarity put emphasis on image structure

but inadequately consider local features of image. So their application fields are limited and performances are unstable. If dividing an image into more meaningful structural blocks

the impaction of local features on image quality can be represented adequately and metric performance can be improved greatly. Based on these considerations

this paper proposes a new quality index using local character. It is implemented by three steps. Firstly

the image is divided into separate meaningful blocks according to a new image division algorithm. Different blocks represent different structures of the image. Secondly

the gradient of the image is used to weigh the influence of different pixels

and then the structural similarities of corresponding blocks between the reference image and distorted image are calculated. Finally

the ultimate image quality is calculated by combining structural similarities of all blocks according to their weights. The experiments show that the proposed metric is more reasonable and stable than traditional methods

and could be used in more application fields.