结合感知特征和自然场景统计的无参考图像质量评价

为了更有效的评价各种失真类型的图像，本文提出了一种新颖的通用型无参考图像质量评价方法，它采取学习感知特征和空域自然统计特征相结合的方法来构建图像质量评价模型。方法是在提取显著分块的36个空域自然统计特征的基础上，增加基于相位一致性熵、基于相位一致性均值、梯度均值以及失真图像的熵四个感知特征，采用支持向量机回归的学习方式来构建图像特征与人的主观分数的映射关系，进而根据所提取特征预测图像质量。在LIVE图像库上的实验表明，文中算法预测质量分数与人的主观分数具有较高的一致性，基本呈线性关系，鲁棒性较好，运行时间较短，综合性能较好。

Blind image quality assessment based on perceptual features and natural scene statistics

In order to efficiently evaluate different kinds of distorted images,a novel general-purpose blind/no-reference image quality assessment is proposed,which combines perceptual features with spacial natural statistics features to construct image quality assessment model.Four perceptual features-phase congruency entropy,mean phase congruency,mean gradient and entropy of the distorted images are selected besides the 36 spacial natural statistics features of sharp patches.features.Support Vector Machine Regression(SVR) is adopted to build relationship between image features and quality scores,yielding a measure of image quality.Experimental results in LIVE database show that the proposed method accords closely with human subjective judgment.It has good robustness and short running time.