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Image quality assessment based on multi-order local features description, modeling and quantification

Ding, Yong; Zhao, Xinyu; Zhang, Zhi LU and Dai, Hang (2017) In IEICE Transactions on Information and Systems E100D(6). p.1303-1315
Abstract

Image quality assessment (IQA) plays an important role in quality monitoring, evaluation and optimization for image processing systems. However, current quality-Aware feature extraction methods for IQA can hardly balance accuracy and complexity. This paper introduces multi-order local description into image quality assessment for feature extraction. The first-order structure derivative and high-order discriminative information are integrated into local pattern representation to serve as the quality-Aware features. Then joint distributions of the local pattern representation are modeled by spatially enhanced histogram. Finally, the image quality degradation is estimated by quantifying the divergence between such distributions of the... (More)

Image quality assessment (IQA) plays an important role in quality monitoring, evaluation and optimization for image processing systems. However, current quality-Aware feature extraction methods for IQA can hardly balance accuracy and complexity. This paper introduces multi-order local description into image quality assessment for feature extraction. The first-order structure derivative and high-order discriminative information are integrated into local pattern representation to serve as the quality-Aware features. Then joint distributions of the local pattern representation are modeled by spatially enhanced histogram. Finally, the image quality degradation is estimated by quantifying the divergence between such distributions of the reference image and those of the distorted image. Experimental results demonstrate that the proposed method outperforms other state-of-The-Art approaches in consideration of not only accuracy that is consistent with human subjective evaluation, but also robustness and stability across different distortion types and various public databases. It provides a promising choice for image quality assessment development.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Feature extraction, Image quality assessment, Image quality degradation, Local pattern representation, Visual perception
in
IEICE Transactions on Information and Systems
volume
E100D
issue
6
pages
13 pages
publisher
The Institute of Electronics, Information and Communication Engineers
external identifiers
  • scopus:85020115595
  • wos:000405673300017
ISSN
0916-8532
DOI
10.1587/transinf.2016EDP7244
language
English
LU publication?
yes
id
8707ccd9-b759-465a-9245-158500b02621
date added to LUP
2017-06-27 14:19:13
date last changed
2017-09-18 11:43:17
@article{8707ccd9-b759-465a-9245-158500b02621,
  abstract     = {<p>Image quality assessment (IQA) plays an important role in quality monitoring, evaluation and optimization for image processing systems. However, current quality-Aware feature extraction methods for IQA can hardly balance accuracy and complexity. This paper introduces multi-order local description into image quality assessment for feature extraction. The first-order structure derivative and high-order discriminative information are integrated into local pattern representation to serve as the quality-Aware features. Then joint distributions of the local pattern representation are modeled by spatially enhanced histogram. Finally, the image quality degradation is estimated by quantifying the divergence between such distributions of the reference image and those of the distorted image. Experimental results demonstrate that the proposed method outperforms other state-of-The-Art approaches in consideration of not only accuracy that is consistent with human subjective evaluation, but also robustness and stability across different distortion types and various public databases. It provides a promising choice for image quality assessment development.</p>},
  author       = {Ding, Yong and Zhao, Xinyu and Zhang, Zhi and Dai, Hang},
  issn         = {0916-8532},
  keyword      = {Feature extraction,Image quality assessment,Image quality degradation,Local pattern representation,Visual perception},
  language     = {eng},
  month        = {06},
  number       = {6},
  pages        = {1303--1315},
  publisher    = {The Institute of Electronics, Information and Communication Engineers},
  series       = {IEICE Transactions on Information and Systems},
  title        = {Image quality assessment based on multi-order local features description, modeling and quantification},
  url          = {http://dx.doi.org/10.1587/transinf.2016EDP7244},
  volume       = {E100D},
  year         = {2017},
}