SIFT (Scale Invariant Feature Transform) points are scale-space extreme points, representing local minutiae structure features in the Gaussian scale space. SIFT intensity, as a novel no-ref erence metric, is feasible to assess various common distortions without the access to reference images. The metric introduces image preprocessing: neighborhood enhancement through contrast enhancement of adjacent pixels to reduce false SIFT points triggered by random signals; double-size image magnification through linear interpolation to amplify distortion effects to improve its sensitivity to image q uality. SIFT intensity is defined as the number of SIFT points in a unit region and is calculated based on the first octave of the difference-of-Gaussian scale space. Experimental results demonstrate that SIFT intensity is superior to existing classic no-referenc e metrics and can be used to assess different distortions.