为克服现有特征相似性( FSIM) 图像质量评价算法对图像信息无序部分及边缘信息度量能力的不足，利用人类视觉系统的内在推导机制，提出基于人类视觉系统的特征相似性图像质量评价算法HFSIM． 该算法采用自回归预测模型分解并解读图像内容的预测部分和无序部分; 联合FSIM 与边缘结构相似性算法度量预测部分，采用多尺度峰值信噪比( PSNR) 度量无序部分的衰减情况，最后根据噪声能量融合图像信息预测部分与无序部分的评价结果得到图像质量评价． 在6 个公开基准数据库上的实验结果表明，该算法与人类主观感知具有高度的一致性，且在各类型失真图像的评价上具有较好的性能．

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.