NDLI: Image quality assessment via spatial-transformed domains multi-feature fusion

Content Provider	IET Digital Library
Author	Yu, Miaomiao Zheng, Yuanlin Liao, Kaiyang Tang, Zhisen
Abstract	The basis of image processing is to evaluate and monitor image quality using algorithms rather than subjective methods. Conventional gradient operators have been popularly used in previous image quality assessment tasks to reflect the edge contour of an image, while there are some obvious defects in terms of the selection of mask scale and direction. Some improved versions are also less than ideal since they fail to consider the gradient information of the same pixel in different directions at the same time. The authors adopt a powerful gradient operator that can simultaneously capture edge information in all four directions at the same pixel point with more relevant values being considered instead of selecting the maximum in these four directions. Furthermore, four complementary types of features extracted from the spatial and transform domains are considered. A set of 12-dimensional feature vectors is generated for each image by multi-feature fusion. Ultimately, random forest regression technique is employed to train their model and then map the distortion effects to the prediction scores. The experimental results show that the proposed FVC-G has better overall performance, more powerful cross-database operation capability, and higher visual consistency than other advanced methods.
Starting Page	648
Ending Page	657
Page Count	10
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 4, Mar (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6417
Journal	IET Image Processing
Publisher Date	2019-11-08
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	12-Dimensional Feature Vector Generation Computer Vision And Image Processing Technique Cross-database Operation Capability Edge Contour Information Feature Extraction FVC-G Gradient Information Operators Gradient Method Image Fusion Image Processing Image Quality Assessment Task Image Recognition Integral Transforms in Numerical Analysis Interpolation And Function Approximation Knowledge Engineering Technique Learning in AI Linear Algebra Mask Scale Numerical Analysis Optimisation Technique Random Forest Random Forest Regression Technique Regression Analysis Spatial-transformed Domain Multifeature Fusion Statistics Subjective Method Transform Vector
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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