As the existing image quality evaluation methods of feature similarity ( FSIM) is inefficient in image information uncertainty measurement and edge information detection,a novel algorithm named HFSIM is proposed on the basis of the internal generative mechanism of human visual system ( HVS) .In this algorithm,the auto-regressive ( AR) model is employed to decompose distorted images,and the original image is decomposed into two portions,one is the predicted portion and the other is the disorderly portion.By combining FSIM with edge structural similarity ( ESSIM) algorithm,the predicted portion of image is measured,and,by employing the multi-scale peak signal-to-noise ratio ( PNSR) ,the distortion of the disorderly portion is measured.Finally,the overall image quality score is obtained according to the above-mentioned measured results of the predicted and the disorderly portions.It is found from the experiments on six public benchmark databases that the proposed algorithm is highly consistent with human perception,and that it possesses high performance in the assessment of different types of distorted images.