鉴于生物视觉特征对于图像的良好表征能力，提出了一种基于生物视觉特征的无参考型图像质量评价方法。对生物视觉ST模型进行了研究和分析，完成了对图像的稀疏化表示[利用最小二乘支持向量机回归方法训练生物视觉特征到图像质量的映射关系，获得能够预测图像质量的回归器[通过学习的回归器完成了对图像质量的评价。基于LIVE图像库的实验结果表明，该方法对于特定失真和交叉失真的预测误差分别为2%和5%左右，并且与目前技术条件下的质量评价方法相比具有很好的精确性和单调性。

As the biological vision features show superior performance to images representation, a nonreference image quality assessment approach based on biological vision features is proposed. The standard model of biological vision is studied and analyzed, and the sparse representation of image is accomplished through the model.The mapping correlation between biological vision features and image quality scores is trained with the regression technique of least squaresupport vector machine, which gains the regressor that can predict the image quality.The score of image quality assessment is accomplished with the trained regressor at last. The experimental results based on LIVE database show that the proposed approach has predicting error of 2% and 5% for specific distortion and crossvalidation distortion respectively，and exhibits a superior accuracy and monotonicity compared to stateoftheart quality assessment approaches.