|Table of Contents|

Full Reference Color Image Quality Assessment Method via Low-level Features Combination with Extreme Learning Machine(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2022年04期
Page:
91-101
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Full Reference Color Image Quality Assessment Method via Low-level Features Combination with Extreme Learning Machine
Author(s):
Ma Yuemei; Fu Hao; Liu Guojun; Yang Ling; Wei Lili
(School of Mathematics and Statistics,Ningxia University,Yinchuan 750021,China)
Keywords:
color image quality assessment; low-level features; local binary pattern; gradient; structural contrast index; extreme learning machine
PACS:
TP391.41; TP181
DOI:
10.3969/j.issn.1001-4616.2022.04.013
Abstract:
As an image quality monitoring and evaluation tool,image quality assessment(IQA)plays an important role in various image processing systems. The ideal IQA method should be consistent with human visual system(HVS). Suppose HVS understanding an image mainly according to its low-level features,a novel Full Reference(FR)color IQA method. Firstly,four different types of low-level feature maps are extracted,namely structural contrast index(SCI),gradient,local binary pattern(LBP),and chroma,which are used to characterize different feature attributes of the image. Secondly,different feature pooling strategies are employed to process each type of features respectively,and a set of similar feature vectors are formed as the detector of image quality. Then,extreme learning machine(ELM)is used to establish regression model and map the feature vectors into an objective quality score. Finally,extensive experiments performed on five benchmark IQA databases and compared with eight state-of-the-art FR IQA metrics. The results demonstrate that the overall performance of proposed method is better than other methods,and can effectively improve the accuracy of IQA index on most of distortions.

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Last Update: 2022-12-15