Revisiting gamut expansion for color space conversion

Cameras and image-editing software are capable of processing images in the wide-gamut ProPhoto color space, which encompasses 90% of all visible colors. However, when images are prepared for sharing, this rich color representation is transformed and clipped to fit within the smaller-gamut standard RGB (sRGB) color space, which represents only 30% of visible colors. Recovering the lost color information poses a challenge due to this clipping procedure. We propose three methods to address this issue. The first method proposes a deep neural network specifically trained for wide-gamut color restoration, utilizing datasets generated by a software-based camera image signal processor that produces pairs of ProPhoto and sRGB images. The second method implements a technique that incorporates a small set of color values sampled from the original ProPhoto image that is saved together with the final smaller-gamut sRGB image. The method then uses the subsampled wide-gamut color values to estimate the original ProPhoto image from the sRGB image. The third method proposes a lightweight multi-layer perceptron (MLP) trained on pairs of ground truth and clipped ProPhoto values during the gamut compression phase. The MLP is saved as metadata in the sRGB image and can later be used to predict and restore the original wide-gamut colors during the gamut expansion phase. Additionally, we have created several large-scale public datasets of wide-gamut/small-gamut image pairs to support research on color space conversion.