The current machine vision-based roughness measurement methods use image metrics with few considerations, no reference making the prediction model accuracy limited and highly influencedby the brightness of the light source. To address this problem, the article proposes a roughness measurement method based on a full-reference color image quality algorithm. Based on the analysis of rough surface imaging mechanism, this method introduces structural information based on visual saliency-induced index(VSI)and proposes an image quality assessment algorithm based on visual saliency structural index(VSIS). Meanwhile, a surface roughness measurement device for grinding samples based on the image quality assessment algorithm is designed. The experimental results show that the proposed VSIS image quality assessment algorithm has a significant correlation with surface roughness(R。). The curve relationship, obtained through the least squares method, enables low-discrepancy and high-precision predictions for grinding samples with a roughness of 0.965 um or greater. The average error and standard deviation for these predictions are measured at 0.111 μm and 0.079 μm，respectively. Compared with the roughness-correlated image characteristic index considering a single factor,VSIS has a better comprehensive performance and can overcome the influence of light source brightness to a certain extent. The method provides an alternative way for non-contact roughness measurement.