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Volume 34, Issue 6
Image Quality Assessment Based on Contour and Region

Chen Huang, Ming Jiang & Tingting Jiang

DOI: 10.4208/jcm.1611-m2016-0534

J. Comp. Math., 34 (2016), pp. 705-722.

Published online: 2016-12

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  • Abstract

Image Quality Assessment (IQA) is a fundamental problem in image processing. It is a common principle that human vision is hierarchical: we first perceive global structural information such as contours then focus on local regional details if necessary. Following this principle, we propose a novel framework for IQA by quantifying the degenerations of structural information and region content separately, and mapping both to obtain the objective score. The structural information can be obtained as contours by contour detection techniques. Experiments are conducted to demonstrate its performance in comparison with multiple state-of-the-art methods on two large scale datasets.

  • Keywords

Image quality assessment, Contour detection, Image segmentation.

  • AMS Subject Headings

68U10, 94A08.

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address

chenhuang@pku.edu.cn (Chen Huang)

ming-jiang@pku.edu.cn (Ming Jiang)

ttjiang@pku.edu.cn (Tingting Jiang)

  • BibTex
  • RIS
  • TXT
@Article{JCM-34-705, author = {Huang , ChenJiang , Ming and Jiang , Tingting}, title = {Image Quality Assessment Based on Contour and Region}, journal = {Journal of Computational Mathematics}, year = {2016}, volume = {34}, number = {6}, pages = {705--722}, abstract = {

Image Quality Assessment (IQA) is a fundamental problem in image processing. It is a common principle that human vision is hierarchical: we first perceive global structural information such as contours then focus on local regional details if necessary. Following this principle, we propose a novel framework for IQA by quantifying the degenerations of structural information and region content separately, and mapping both to obtain the objective score. The structural information can be obtained as contours by contour detection techniques. Experiments are conducted to demonstrate its performance in comparison with multiple state-of-the-art methods on two large scale datasets.

}, issn = {1991-7139}, doi = {https://doi.org/10.4208/jcm.1611-m2016-0534}, url = {http://global-sci.org/intro/article_detail/jcm/9821.html} }
TY - JOUR T1 - Image Quality Assessment Based on Contour and Region AU - Huang , Chen AU - Jiang , Ming AU - Jiang , Tingting JO - Journal of Computational Mathematics VL - 6 SP - 705 EP - 722 PY - 2016 DA - 2016/12 SN - 34 DO - http://doi.org/10.4208/jcm.1611-m2016-0534 UR - https://global-sci.org/intro/article_detail/jcm/9821.html KW - Image quality assessment, Contour detection, Image segmentation. AB -

Image Quality Assessment (IQA) is a fundamental problem in image processing. It is a common principle that human vision is hierarchical: we first perceive global structural information such as contours then focus on local regional details if necessary. Following this principle, we propose a novel framework for IQA by quantifying the degenerations of structural information and region content separately, and mapping both to obtain the objective score. The structural information can be obtained as contours by contour detection techniques. Experiments are conducted to demonstrate its performance in comparison with multiple state-of-the-art methods on two large scale datasets.

Chen Huang, Ming Jiang & Tingting Jiang. (2020). Image Quality Assessment Based on Contour and Region. Journal of Computational Mathematics. 34 (6). 705-722. doi:10.4208/jcm.1611-m2016-0534
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