A Lightweight MLP for Color Loss Recovery

Hoang M. Le 1 , Brian Price 2 , Scott Cohen 2 , Michael S. Brown 1

1York University, Canada
2Adobe Research

Paper Code Dataset


Cameras and image-editing software often process images in the wide-gamut ProPhoto color space, encompassing 90% of all visible colors. However, when images are encoded for sharing, this color-rich representation is transformed and clipped to fit within the small-gamut standard RGB (sRGB) color space, representing only 30% of visible colors. Recovering the lost color information is challenging due to the clipping procedure. Inspired by neural implicit representations for 2D images, we propose a method that optimizes a lightweight multi-layer-perceptron (MLP) model during the gamut reduction step to predict the clipped values. GamutMLP takes approximately 2 seconds to optimize and requires only 23 KB of storage. The small memory footprint allows our GamutMLP model to be saved as metadata in the sRGB image---the model can be extracted when needed to restore wide-gamut color values. We demonstrate the effectiveness of our approach for color recovery and compare it with alternative strategies, including pre-trained DNN-based gamut expansion networks and other implicit neural representation methods. As part of this effort, we introduce a new color gamut dataset of 2200 wide-gamut/small-gamut images for training and testing.

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    author    = {Le, Hoang M. and Price, Brian and Cohen, Scott and Brown, Michael S.},
    title     = {GamutMLP: A Lightweight MLP for Color Loss Recovery},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {18268-18277}


This study was funded in part by the Canada First Research Excellence Fund for the Vision: Science to Applications (VISTA) program, an NSERC Discovery Grant, and an Adobe Gift Award.