NDLI: Neural network-based image quality comparator without collecting the human score for training

Content Provider	IET Digital Library
Author	Bao, Long Panetta, Karen Agaian, Sos
Abstract	Emulating human behaviours in automated image quality assessment (IQA) enables a comparator framework to remove the differences in human bias naturally. Based on the observation of the practical applications of IQA, this study focuses on similar-content image quality comparison based on a new image quality comparator (IQC). Outstanding proven IQAs can be utilised in this comparator to achieve a new non-linear combination strategy to boost the IQAs' performance in image quality comparison. For both input images to be compared, proven IQAs are utilised to obtain nine features from each image, yielding 18 total features. Then, a four-layer comparison network conducts a classification task to indicate which input image has better quality. In the training phase, the commonly used human scores as training labels are replaced with pairwise comparison results that are automatically generated from assigned distortion level differences. By not utilising human score in training phase, this IQC shows two advantages: (i) it removes huge labor and time cost to collect the human scores and (ii) it solves the problem of over-fitting benefiting from simplicity of creating a large image training dataset. Furthermore, the experimental tests and cross-dataset validation comparison tests demonstrate its impressive performance.
Starting Page	1787
Ending Page	1793
Page Count	7
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 9, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0809
Journal	IET Image Processing
Publisher Date	2020-03-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Assigned Distortion Level Differences Automated Image Quality Assessment Comparator Framework Comparison Accuracy Computer Vision And Image Processing Technique Cross-dataset Validation Comparison Tests Feature Extraction Four-layer Comparison Network Human Behaviour Human Bias Image Classification Image Recognition Image Training Dataset Input Image IQA Performance IQC Knowledge Engineering Technique Learning in AI Neural Computing Technique Neural Nets Neural Network-based Image Quality Comparator Nonlinear Combination Strategy Pairwise Comparison Result Providing Accurate Evaluation Result Similar-content Image Quality Comparison Statistics Training Labels Training Phase Utilising Human Score Widely-adapted Image Quality Measure Tests
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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