Blind image quality assessment (BIQA) aims to design a model that can accurately evaluate the quality of the distorted image without any information about its reference image. Previous studies have shown that gradients and textures of image is widely used in image quality evaluation tasks. However, few studies used the joint statistics of gradient and texture information to evaluate image quality. Considering the visual perception characteristics of the human visual system, we develop a novel general-purpose BIQA model via two sets of complementary perception features. Specifically, the joint statistical histograms of gradient and texture are extracted as the first set of features, and the second set of features is extracted using the local binary pattern (LBP) operator. After extracting two groups of complementary quality-aware features, the feature vectors are sent to the support vector regression machine to establish the nonlinear relationship between quality-aware features and quality scores. A large number of experiments on seven large benchmark databases show that the proposed BIQA model has higher accuracy, better generalization properties and lower computational complexity than the relevant state-of-the-art BIQA metrics.

盲图质量评估（BIQA）旨在设计一种模型，该模型可以准确地评估失真图像的质量，而无需任何有关其参考图像的信息。先前的研究表明，图像的渐变和纹理已广泛用于图像质量评估任务中。但是，很少有研究使用梯度和纹理信息的联合统计来评估图像质量。考虑到人类视觉系统的视觉感知特性，我们通过两组互补的感知功能开发了一种新颖的通用BIQA模型。具体来说，提取梯度和纹理的联合统计直方图作为第一组特征，并使用局部二进制模式（LBP）运算符提取第二组特征。提取两组互补的质量意识特征后，将特征向量发送到支持向量回归机，以建立质量意识特征与质量得分之间的非线性关系。在七个大型基准数据库上进行的大量实验表明，与相关的最新BIQA指标相比，所提出的BIQA模型具有更高的准确性，更好的泛化特性和更低的计算复杂度。