A novel gamut expansion method based on combined global–local mapping for sRGB‐to‐ProPhoto conversion

Abstract

Abstract With the development of display technology, more displays can cover the wider gamut, but most of the content they show is based on a small gamut. It is significant to employ gamut expansion (GE) to expand the small gamut images to a wider target gamut. Most of the existing GE methods only use global or local operations to realize the mapping from small gamut to wide gamut. However, the utilization of both global information and local feature is important for GE. In this article, the authors propose a combined global‐local gamut expansion network (G‐LGENet) for mapping the input standard RGB (sRGB) images to wider ProPhoto RGB space. In G‐LGENet, the global colour mapping module first extracts and fuses the global colour priors and learns the mapping of colour information for the corresponding pixels. And then, the local enhancement (LE) is designed to extract the local colour information between the corresponding pixel and neighbourhood pixels. The experimental results on a sRGB‐to‐ProPhoto dataset have demonstrated that the proposed G‐LGENet outperforms the other excellent GE methods qualitatively and visually.

Keywords

• image colour analysis
• image enhancement
• image restoration
• learning (artificial intelligence)