Bias in Automated Image Colorization: Metrics and Error Types

Frank Stapel; Floris Weers; Doina Bucur

doi:10.48550/arXiv.2202.08143

Bias in Automated Image Colorization: Metrics and Error Types

Frank Stapel, Floris Weers, Doina Bucur

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Abstract

We measure the color shifts present in colorized images from the ADE20K dataset, when colorized by the automatic GAN-based DeOldify model. We introduce fine-grained local and regional bias measurements between the original and the colorized images, and observe many colorization effects. We confirm a general desaturation effect, and also provide novel observations: a shift towards the training average, a pervasive blue shift, different color shifts among image categories, and a manual categorization of colorization errors in three classes.

Original language	English
DOIs	https://doi.org/10.48550/arXiv.2202.08143
Publication status	Published - 16 Feb 2022

Keywords

cs.CV
68T45
I.4.4

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2202.08143v1Submitted version, 3 MB

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title = "Bias in Automated Image Colorization: Metrics and Error Types",

abstract = "We measure the color shifts present in colorized images from the ADE20K dataset, when colorized by the automatic GAN-based DeOldify model. We introduce fine-grained local and regional bias measurements between the original and the colorized images, and observe many colorization effects. We confirm a general desaturation effect, and also provide novel observations: a shift towards the training average, a pervasive blue shift, different color shifts among image categories, and a manual categorization of colorization errors in three classes.",

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N2 - We measure the color shifts present in colorized images from the ADE20K dataset, when colorized by the automatic GAN-based DeOldify model. We introduce fine-grained local and regional bias measurements between the original and the colorized images, and observe many colorization effects. We confirm a general desaturation effect, and also provide novel observations: a shift towards the training average, a pervasive blue shift, different color shifts among image categories, and a manual categorization of colorization errors in three classes.

AB - We measure the color shifts present in colorized images from the ADE20K dataset, when colorized by the automatic GAN-based DeOldify model. We introduce fine-grained local and regional bias measurements between the original and the colorized images, and observe many colorization effects. We confirm a general desaturation effect, and also provide novel observations: a shift towards the training average, a pervasive blue shift, different color shifts among image categories, and a manual categorization of colorization errors in three classes.

KW - cs.CV

KW - 68T45

KW - I.4.4

U2 - 10.48550/arXiv.2202.08143

DO - 10.48550/arXiv.2202.08143

M3 - Preprint

BT - Bias in Automated Image Colorization

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Bias in Automated Image Colorization: Metrics and Error Types

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Keywords

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