This paper presents a high-performance general-purpose no-reference (NR) image quality assessment (IQA) method based on image entropy. The image features are extracted from two domains. In the spatial domain, the mutual information between different color channels and the two-dimensional entropy are calculated. In the frequency domain, the statistical characteristics of the two-dimensional entropy and the mutual information of the filtered subband images are computed as the feature set of the input color image. Then, with all the extracted features, the support vector classifier (SVC) for distortion classification and support vector regression (SVR) are utilized for the quality prediction, to obtain the final quality assessment score. The proposed method, which we call entropy-based no-reference image quality assessment (ENIQA), can assess the quality of different categories of distorted images, and has a low complexity. The proposed ENIQA method was assessed on the LIVE and TID2013 databases and showed a superior performance. The experimental results confirmed that the proposed ENIQA method has a high consistency of objective and subjective assessment on color images, which indicates the good overall performance and generalization ability of ENIQA. The implementation is available on github https://github.com/jacob6/ENIQA.

中文翻译：

---

无参考彩色图像质量评估：从熵到感知质量

本文提出了一种基于图像熵的高性能通用无参考（NR）图像质量评估（IQA）方法。图像特征是从两个域中提取的。在空间域中，计算不同颜色通道之间的互信息和二维熵。在频域中，将二维熵的统计特性和滤波后的子带图像的互信息计算为输入彩色图像的特征集。然后，利用所有提取的特征，将用于失真分类的支持向量分类器（SVC）和支持向量回归（SVR）用于质量预测，以获得最终质量评估分数。所提出的方法，我们称为基于熵的无参考图像质量评估（ENIQA），可以评估不同类别的失真图像的质量，并且复杂度较低。在LIVE和TID2013数据库上对提议的ENIQA方法进行了评估，并显示出优异的性能。实验结果表明，所提出的ENIQA方法在彩色图像的主观评价上具有较高的一致性，表明ENIQA具有良好的综合性能和泛化能力。该实现在github https://github.com/jacob6/ENIQA上可用。这表明ENIQA具有良好的整体性能和泛化能力。该实现在github https://github.com/jacob6/ENIQA上可用。这表明ENIQA具有良好的整体性能和泛化能力。该实现在github https://github.com/jacob6/ENIQA上可用。