
Distortions introduced during the reproduction of digital images can lead to substantial changes in their color composition. The motivations for altering images range from practical purposes, such as image compression and color quantization to reduce file size, to more aesthetic applications like style transfer using generative AI. In this work, we investigate how the reproduction of color images affects material appearance, in particular, the perception of gloss and translucency. We applied different image quality distortions to natural images of glossy and translucent objects. Additionally, we Ghiblified them – a recent viral social media phenomenon of mimicking the Japanese anime style using generative AI style transfer. Afterward, we conducted a series of user studies to evaluate the fidelity of gloss and translucency reproduction. The experimental results represent how the reproductions are perceived by image quality metrics and open up a new direction for material appearance studies.
Mobina Mobini, Olga Cherepkova, Davit Gigilashvili, "Ghiblification and Color Richness in Material Appearance: How Human Observers and Image Quality Metrics Perceive Them?" in Color and Imaging Conference, 2025, pp 136 - 141, https://doi.org/10.2352/CIC.2025.33.1.26