建立图像质量客观评价模型对于图像编码、增强、重建以及分析等领域具有重要的现实意义。鉴于传统的图像质量评价方法的评价结果与主观感知存在较大的误差等缺陷，为此从分形角度考虑，并兼顾人眼视觉特性，首先提取了分形维数作为图像质量的评价指标；然后从非线性角度来表征引起人眼视觉敏感变化的图像亮度以及纹理信息，并将能准确反映图像质量变化的空隙度参数作为有效补偿；最后采用线性回归分析直接对图像进行建模，并将分形维数差值和空隙度差值两分量表示在统一的模型中。实验证明，相对于传统的PSNR和SSIM评价指标而言，该评价模型不仅对于不同类型的失真、相同失真类型的不同失真级别的图像能够准确进行评估，而且与主观评价值（MOS）具有更好的关联性，即与人眼视觉感受具有较高的吻合性，同时能够实现对图像质量进行全面、科学的评价。

It is very urgent to build an evaluation criterion in image compression, enhancement, restoration and analysis fields.In this paper, fractal dimension is utilized to be an image quality assessment index and describe image luminance and texture characteristics from non-linear aspects.Lacunarity changes can also describe image quality.Therefore fractal dimension difference and lacunarity difference are integrated into one single mathematics model by linear regression analysis.Experiment results show that against traditional model such as PSNR, SSIM and so on, the proposed approach can not only assess different distortion types, same distortion type with different distortion levers accurately, but also has stronger correlation with MOS, more agreement with the perceptual of human beings, and can assess image quality accurately and effectively.