  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 the color channels and the two-dimensional entropy are
calculated. In the frequency domain, the two-dimensional entropy and the mutual
information of the filtered sub-band 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 source code is available on github
https://github.com/jacob6/ENIQA.
