Current methods of image quality assessment only can assess the quality of images under the same type of image distortion. In order to fix such weaknesses, this paper is designed based on the image features of natural scene statistics and proposes a new metric method using high-pass filter for detecting features. The approach computes locally the normalized luminance; selects features such as the difference of RGB channels via high-pass filter, image gradient, sharpness, contrast, etc.; and analyzes and gathers features in the metric method trained by logistic regression. Experimental results show that the proposed method can work efficiently under multiple distortion types and is significantly better than current no-reference image quality assessment methods under the test sets, which gather multiple distortion types.