Image quality assessment (IQA) is one of the basic research issues in the field of computer vision. At present, most image quality models are constructed based on grayscale images, and color image quality assessment is still an open issue in the field of IQA. The key of color image quality assessment research is to construct a quantitative description of color information consistent with human color cognition. This paper constructs a color image quality assessment model based on colornames(CN). It maps each pixel value of the image to a CN probability vector, uses the Wasserstein distance to calculate the perceived color difference of two images, uses the lightness and gradient features, and uses the saliency weighting in the pooling stage to obtain the objective image quality scores. The experimental results on the public test databases show that the proposed model performs best on TID2008, TID2013 and the latest KADID-10k databases, with SROCC values of 0.900 9, 0.890 1 and 0.863 7, respectively. The overall assessment effect is comparable to the current best traditional method (Non-deep learning method). But for color distortion, it has obvious advantages.