Quality Index for Benchmarking Image Inpainting Algorithms with Guided Regional Statistics

Song LIANG  Leida LI  Bo HU  Jianying ZHANG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.7   pp.1430-1433
Publication Date: 2019/07/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDL8206
Type of Manuscript: LETTER
Category: Image Processing and Video Processing
Keyword: 
quality evaluation,  image inpainting,  GRS,  gradient magnitude,  naturalness,  

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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.