Bias in Automated Image Colorization: Metrics and Error Types

Doina Bucur; Floris Weers; Frank Stapel

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Bias in Automated Image Colorization: Metrics and Error Types

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arxiv 2202.08143 v1 pith:JLL4CJTA submitted 2022-02-16 cs.CV

Frank Stapel , Floris Weers , Doina Bucur This is my paper · ORCID

classification cs.CV

keywords colorizationcolorizedbiascolorimageimagesshiftshifts

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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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