How good are RGB cameras retrieving colors of natural scenes and paintings?—A study based on hyperspectral imaging

• Autor(a) principal: Linhares, João M. M.
• Data de Publicação: 2020
• Outros Autores: Monteiro, José A. R., Bailão, Ana, Cardeira, Liliana, Kondo, Taisei, Nakauchi, Shigeki, Picollo, Marcello, Cucci, Costanza, Casini, Andrea, Stefani, Lorenzo, Nascimento, Sérgio M. C.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/1822/68297
• topic: hyperspectral imaging; natural scenes; paintings; chromatic errors; color difference; number of colors; Ciências Naturais::Ciências Físicas; Science & Technology

RGB digital cameras (RGB) compress the spectral information into a trichromatic system capable of approximately representing the actual colors of objects. Although RGB digital cameras follow the same compression philosophy as the human eye (OBS), the spectral sensitivity is different. To what extent they provide the same chromatic experiences is still an open question, especially with complex images. We addressed this question by comparing the actual colors derived from spectral imaging with those obtained with RGB cameras. The data from hyperspectral imaging of 50 natural scenes and 89 paintings was used to estimate the chromatic differences between OBS and RGB. The corresponding color errors were estimated and analyzed in the color spaces CIELAB (using the color difference formulas Δ<i>E^*_ab</i> and CIEDE2000), <i>J_za_zb_z</i>, and iCAM06. In CIELAB the most frequent error (using Δ<i>E^*_ab</i>) found was 5 for both paintings and natural scenes, a similarity that held for the other spaces tested. In addition, the distribution of errors across the color space shows that the errors are small in the achromatic region and increase with saturation. Overall, the results indicate that the chromatic errors estimated are close to the acceptance error and therefore RGB digital cameras are able to produce quite realistic colors of complex scenarios.