No-reference image quality assessment based on the AdaBoost BP neural network in the wavelet domain

Yan, Junhua, Bai, Xuehan, Zhang, Wanyi, Xiao, Yongqi, Chatwin, Chris, Young, Rupert and Birch, Phil (2019) No-reference image quality assessment based on the AdaBoost BP neural network in the wavelet domain. Journal of Systems Engineering and Electronics, 30 (2). pp. 223-237. ISSN 1004-4132

[img] PDF - Published Version
Download (5MB)
[img] PDF - Other
Restricted to SRO admin only

Download (5MB)
[img] PDF - Accepted Version
Restricted to SRO admin only

Download (2MB)

Abstract

Considering the relatively poor robustness of quality scores for different types of distortion and the lack of mechanism for determining distortion types, a no-reference image quality assessment (NR-IQA) method based on the AdaBoost BP Neural Network in Wavelet domain (WABNN) is proposed. A 36-dimensional image feature vector is constructed by extracting natural scene statistics (NSS) features and local information entropy features of the distorted image wavelet sub-band coefficients in three scales. The ABNN classifier is obtained by learning the relationship between image features and distortion types. The ABNN scorer is obtained by learning the relationship between image features and image quality scores. A series of contrast experiments are carried out in the LIVE database and TID2013 database. Experimental results show the high accuracy of the distinguishing distortion type, the high consistency with subjective scores and the high robustness of the method for distorted images. Experiment results also show the independence for the database and the relatively high operation efficiency of this method.

Item Type: Article
Keywords: image quality assessment, AdaBoost BP neural network (ABNN), wavelet transform, natural scene statistics (NSS), local information entropy.
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Research Centres and Groups: Industrial Informatics and Signal Processing Research Group
Subjects: Q Science > QA Mathematics > QA0273 Probabilities. Mathematical statistics
Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK6680.5 Digital video. General works
U Military Science > UG Military engineering. Air forces. Air Warfare. Military astronautics. Space warfare. Space surveillance > UG0622 Air forces. Air warfare > UG0730 Air defences
U Military Science > UG Military engineering. Air forces. Air Warfare. Military astronautics. Space warfare. Space surveillance > UG0622 Air forces. Air warfare > UG0760 Aerial reconnaissance
Depositing User: Chris Chatwin
Date Deposited: 24 Apr 2019 09:02
Last Modified: 03 Jul 2019 15:30
URI: http://sro.sussex.ac.uk/id/eprint/83319

View download statistics for this item

📧 Request an update
Project NameSussex Project NumberFunderFunder Ref
Science and Technology on Avionics Integration Laboratory and Aeronautical Science Foundation of China (20155552050),iisp-Visiting FellowNational Natural Science Foundation of China (61471194; 61705104),(61471194; 61705104),