No-reference color image quality assessment: from entropy to perceptual quality

被引:75
作者
Chen, Xiaoqiao [1 ]
Zhang, Qingyi [1 ]
Lin, Manhui [1 ]
Yang, Guangyi [1 ]
He, Chu [1 ,2 ]
机构
[1] Wuhan Univ, Sch Elect Informat, Wuhan 430072, Peoples R China
[2] Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China
基金
中国国家自然科学基金;
关键词
Image entropy; Mutual information; No-reference image quality assessment; Support vector classifier; Support vector regression; ASSESSMENT ALGORITHMS; STATISTICS; INFORMATION; PREDICTION;
D O I
10.1186/s13640-019-0479-7
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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 .
引用
收藏
页数:14
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