Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142325
Title: No reference quality assessment for screen content images with both local and global feature representation
Authors: Fang, Yuming
Yan, Jiebin
Li, Leida
Wu, Jinjian
Lin, Weisi
Keywords: Engineering::Computer science and engineering
Issue Date: 2017
Source: Fang, Y., Yan, J., Li, L., Wu, J., & Lin, W. (2018). No reference quality assessment for screen content images with both local and global feature representation. IEEE Transactions on Image Processing, 27(4), 1600-1610. doi:10.1109/TIP.2017.2781307
Journal: IEEE Transactions on Image Processing
Abstract: 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.
URI: https://hdl.handle.net/10356/142325
ISSN: 1057-7149
DOI: 10.1109/TIP.2017.2781307
Rights: © 2017 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

SCOPUSTM   
Citations

49
Updated on Feb 22, 2021

PublonsTM
Citations

39
Updated on Feb 23, 2021

Page view(s)

15
Updated on Feb 26, 2021

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.