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arXiv · 2502.05402

Convolutional Deep Colorization for Image Compression: A Color Grid Based Approach

Ian Tassin·Kristen Goebel·Brittany Lasher
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Abstract

The search for image compression optimization techniques is a topic of constant interest both in and out of academic circles. One method that shows promise toward future improvements in this field is image colorization since image colorization algorithms can reduce the amount of color data that needs to be stored for an image. Our work focuses on optimizing a color grid based approach to fully-automated image color information retention with regard to convolutional colorization network architecture for the purposes of image compression. More generally, using a convolutional neural network for image re-colorization, we want to minimize the amount of color information that is stored while still being able to faithfully re-color images. Our results yielded a promising image compression ratio, while still allowing for successful image recolorization reaching high CSIM values.

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Ian Tassin, Kristen Goebel, Brittany Lasher. 2025-02-08. Convolutional Deep Colorization for Image Compression: A Color Grid Based Approach. https://arxiv.org/abs/2502.05402

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