Image quality assessment based on multi-order local features description, modeling and quantification
(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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- author
- Ding, Yong ; Zhao, Xinyu ; Zhang, Zhi LU and Dai, Hang
- organization
- publishing date
- 2017-06-01
- 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
- 2024-03-17 16:32:48
@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}},
keywords = {{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}},
doi = {{10.1587/transinf.2016EDP7244}},
volume = {{E100D}},
year = {{2017}},
}