To improve the accuracy of image quality assessment， a full-reference image quality assessment （IQA） model is proposed based on the phase consistency of color appearance scale. First， the image structure information is extracted from vividness， which is an index of color appearance in the CIELAB color space， to obtain a color appearance-based phase consistency value. Subsequently， the contrast similarity map is calculated using the root-mean-square method to obtain the chroma similarity map through the color channel of the color space. Finally， the three image features of phase consistency， contrast， and chromaticity are combined， and the standard deviation method is used for pooling. Consequently， the full-reference IQA computing model is realized. To verify the reliability of this model， experiments were conducted on distorted images in four common image databases， whereby prediction accuracy， computational complexity， and generalization were determined based on four criteria. In the experimental results， the Pearson linear correlation coefficient of this model was the lowest for TID2013 at 0.8781 and highest for LIVE at 0.9616. The Spearman rank correlation coefficient was the lowest for TID2013 at 0.8592 and highest for LIVE at 0.9653. Compared with many existing methods， the proposed IQA model has higher prediction accuracy for visual relationships.