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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.

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Frank Stapel, Floris Weers, Doina Bucur. 2022-02-16. Bias in Automated Image Colorization: Metrics and Error Types. https://arxiv.org/abs/2202.08143

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