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

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Version 2 2023-06-12, 09:05
Version 1 2023-06-09, 17:36
journal contribution
posted on 2023-06-12, 09:05 authored by Junhua Yan, Xuehan Bai, Wanyi Zhang, Yongqi Xiao, Chris ChatwinChris Chatwin, Rupert YoungRupert Young, Phil BirchPhil Birch
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.

Funding

Science and Technology on Avionics Integration Laboratory and Aeronautical Science Foundation of China (20155552050),; iisp-Visiting Fellow; National Natural Science Foundation of China (61471194; 61705104),; (61471194; 61705104),

History

Publication status

  • Published

File Version

  • Published version

Journal

Journal of Systems Engineering and Electronics

ISSN

1004-4132

Publisher

Beijing Institute of Aerospace Information (BIAI)

Issue

2

Volume

30

Page range

223-237

Department affiliated with

  • Engineering and Design Publications

Research groups affiliated with

  • Industrial Informatics and Signal Processing Research Group Publications

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2019-04-24

First Open Access (FOA) Date

2019-07-03

First Compliant Deposit (FCD) Date

2019-04-17

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