A new blind assessment algorithm was put forward for Gaussian Blur. This method selected Gaussian Blur images from an authorized image library, extracted the gradient and conducted Fast Fourier Transform (FFT) and then got a frequency spectrum. It then calculated the initial image, the gradient image and the frequency spectrum, and extracted the edge intensity, the variance and the information entropy as characteristic vectors of each image. GRNN (characteristic vector) was used to build the blur image quality evaluation model which could input characteristic vector and output the calculated Different Mean Opinion Scores (DMOS). Compared with other common algorithms, this method had relatively higher Spearman’s rank correlation coefficient (SROCC) at 0.9086 and Pearson linear correlation coefficient (PLCC) at 0.9033. The results of the calculation, which utilizes the blur image, set of CSIQ and LIVE database in the Matlab environment indicates the calculated DMOS with the new algorithm has high similarity with the DMOS of subjective judgment and is nearly close to human visual judgment.