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Quality Index for Benchmarking Image Inpainting Algorithms with Guided Regional Statistics

Song LIANG, Leida LI, Bo HU, Jianying ZHANG

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Summary :

This letter presents an objective quality index for benchmarking image inpainting algorithms. Under the guidance of the masks of damaged areas, the boundary region and the inpainting region are first located. Then, the statistical features are extracted from the boundary and inpainting regions respectively. For the boundary region, we utilize Weibull distribution to fit the gradient magnitude histograms of the exterior and interior regions around the boundary, and the Kullback-Leibler Divergence (KLD) is calculated to measure the boundary distortions caused by imperfect inpainting. Meanwhile, the quality of the inpainting region is measured by comparing the naturalness factors between the inpainted image and the reference image. Experimental results demonstrate that the proposed metric outperforms the relevant state-of-the-art quality metrics.

Publication
IEICE TRANSACTIONS on Information Vol.E102-D No.7 pp.1430-1433
Publication Date
2019/07/01
Publicized
2019/04/01
Online ISSN
1745-1361
DOI
10.1587/transinf.2018EDL8206
Type of Manuscript
LETTER
Category
Image Processing and Video Processing

Authors

Song LIANG
  China University of Mining and Technology
Leida LI
  China University of Mining and Technology
Bo HU
  China University of Mining and Technology
Jianying ZHANG
  China University of Mining and Technology

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