The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cette lettre présente un indice de qualité objectif pour l’analyse comparative des algorithmes d’inpainting d’images. Sous la direction des masques des zones endommagées, la région limite et la région d'inpainting sont d'abord localisées. Ensuite, les caractéristiques statistiques sont extraites respectivement des régions de frontière et d’inpainting. Pour la région limite, nous utilisons la distribution de Weibull pour ajuster les histogrammes d'amplitude du gradient des régions extérieures et intérieures autour de la limite, et la divergence Kullback-Leibler (KLD) est calculée pour mesurer les distorsions des limites causées par une inpainting imparfaite. Pendant ce temps, la qualité de la région de peinture est mesurée en comparant les facteurs de naturel entre l'image peinte et l'image de référence. Les résultats expérimentaux démontrent que la métrique proposée surpasse les métriques de qualité de pointe pertinentes.
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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Song LIANG, Leida LI, Bo HU, Jianying ZHANG, "Quality Index for Benchmarking Image Inpainting Algorithms with Guided Regional Statistics" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 7, pp. 1430-1433, July 2019, doi: 10.1587/transinf.2018EDL8206.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8206/_p
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@ARTICLE{e102-d_7_1430,
author={Song LIANG, Leida LI, Bo HU, Jianying ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Quality Index for Benchmarking Image Inpainting Algorithms with Guided Regional Statistics},
year={2019},
volume={E102-D},
number={7},
pages={1430-1433},
abstract={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.},
keywords={},
doi={10.1587/transinf.2018EDL8206},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - Quality Index for Benchmarking Image Inpainting Algorithms with Guided Regional Statistics
T2 - IEICE TRANSACTIONS on Information
SP - 1430
EP - 1433
AU - Song LIANG
AU - Leida LI
AU - Bo HU
AU - Jianying ZHANG
PY - 2019
DO - 10.1587/transinf.2018EDL8206
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2019
AB - 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.
ER -