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

EURASIP journal on image and video processing, 2019-09, Vol.2019 (1), p.1-14, Article 77 [Peer Reviewed Journal]

The Author(s) 2019 ;EURASIP Journal on Image and Video Processing is a copyright of Springer, (2019). All Rights Reserved. © 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1687-5281 ;ISSN: 1687-5176 ;EISSN: 1687-5281 ;DOI: 10.1186/s13640-019-0479-7

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Title:
No-reference color image quality assessment: from entropy to perceptual quality
Author: Chen, Xiaoqiao ; Zhang, Qingyi ; Lin, Manhui ; Yang, Guangyi ; He, Chu
Subjects: Biometrics ; Color imagery ; Engineering ; Entropy ; Entropy (Information theory) ; Feature extraction ; Image entropy ; Image filters ; Image Processing and Computer Vision ; Image quality ; Mutual information ; No-reference image quality assessment ; Pattern Recognition ; Quality ; Quality assessment ; Signal,Image and Speech Processing ; Statistical analysis ; Subjective assessment ; Support vector classifier ; Support vector machines ; Support vector regression
Is Part Of: EURASIP journal on image and video processing, 2019-09, Vol.2019 (1), p.1-14, Article 77
Description: 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 .
Publisher: Cham: Springer International Publishing
Language: English
Identifier: ISSN: 1687-5281
ISSN: 1687-5176
EISSN: 1687-5281
DOI: 10.1186/s13640-019-0479-7
Source: Springer Open Access
ROAD: Directory of Open Access Scholarly Resources
ProQuest Central
DOAJ Directory of Open Access Journals

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

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