No Reference Quality Assessment for Screen Content Images With Both Local and Global Feature Representation

In this paper, we propose a novel no reference quality assessment method by incorporating statistical luminance and texture features (NRLT) for screen content images (SCIs) with both local and global feature representation. The proposed method is designed inspired by the perceptual property of the h...

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Bibliographic Details
Published in:IEEE transactions on image processing 2018-04, Vol.27 (4), p.1600-1610
Main Authors: Fang, Yuming, Yan, Jiebin, Li, Leida, Wu, Jinjian, Lin, Weisi
Format: Article
Language:English
Subjects:
Distortion
Feature extraction
global feature
Histograms
Image quality
local feature
Measurement
no reference quality assessment
Quality assessment
Screen content image
visual quality assessment
Visualization
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Summary:In this paper, we propose a novel no reference quality assessment method by incorporating statistical luminance and texture features (NRLT) for screen content images (SCIs) with both local and global feature representation. The proposed method is designed inspired by the perceptual property of the human visual system (HVS) that the HVS is sensitive to luminance change and texture information for image perception. In the proposed method, we first calculate the luminance map through the local normalization, which is further used to extract the statistical luminance features in global scope. Second, inspired by existing studies from neuroscience that high-order derivatives can capture image texture, we adopt four filters with different directions to compute gradient maps from the luminance map. These gradient maps are then used to extract the second-order derivatives by local binary pattern. We further extract the texture feature by the histogram of high-order derivatives in global scope. Finally, support vector regression is applied to train the mapping function from quality-aware features to subjective ratings. Experimental results on the public large-scale SCI database show that the proposed NRLT can achieve better performance in predicting the visual quality of SCIs than relevant existing methods, even including some full reference visual quality assessment methods.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2017.2781307