The Impact of Intrinsic Scene Cues on Perceived Color Transfer Quality

Color transfer adjusts the colors of a source image to match the color characteristics of a reference image, often for artistic or visual enhancement purposes. While most existing methods operate purely on color statistics, the influence of intrinsic scene cues on perceptual quality remains underexplored. This paper investigates the impact of incorporating semantics, illumination, and geometry into color transfer algorithms. To enable this analysis, we introduce a dataset of photorealistic indoor scenes rendered with controlled variations in viewpoint, object arrangement, illumination, and color distribution. Each scene includes intrinsic scene representations capturing illumination, semantic, and geometric properties. A modular framework is proposed to integrate this information into existing methods. Subjective user studies show that intrinsic scene cues significantly influence perceived color transfer quality.