In order to obtain an assessment method for image quality that is consistent with the human visual perception system, this study proposed a no-reference assessment method for blur image quality by simulating the human visual perception system. The proposed method evaluates images of different blurriness by comparing the similarity of their characteristics. First, the test image is blurred by Gaussian functions to different degrees. Second, their detailed information is obtained through the retinal model. Third, singular values are decomposed to measure the intrinsic structures of images. Then, the similarities in details and singular values among the test image and its blurred images are calculated as the characteristic vectors for image blurriness, which are input into a Support Vector Regression (SVR) model for training to generate the proposed assessment method for image quality. Experimental results on benchmark databases show that the proposed method is more consistent with the subjective visual perception of human visual system than the comparison methods.