Color-difference evaluation for 3D objects
Metadata
Show full item recordEditorial
Optical Society of America
Date
2021-07-15Referencia bibliográfica
Lan Jiang... [et al.]. "Color-difference evaluation for 3D objects," Opt. Express 29, 24237-24254 (2021). [https://doi.org/10.1364/OE.432729]
Sponsorship
National Natural Science Foundation of China (NSFC) 61775170; Ministry of Science and Innovation (National Government of Spain); European Research Development Fund (European Union) PID2019-107816GB-I00/SRA/10.13039/501100011033Abstract
A psychophysical experiment using 3D printed samples was conducted to investigate
the change of perceived color differences caused by two different illuminations and two 3D
sample shapes. 150 pairs of 3D printed samples around five CIE color centers [Color Res. Appl.
20, 399–403, 1995], consisting of 75 pairs of spherical samples and 75 pairs of flat samples, with
a wide range of color differences covering from small to large magnitude, were printed by an
Mcor Iris paper-based 3D color printer. Each pair was assessed twice by a panel of 10 observers
using a gray-scale psychophysical method in a spectral tunable LED viewing cabinet with two
types of light sources: diffuse lighting with and without an additional overhead spotlight. The
experimental results confirmed that the lighting conditions had more effect on the perceived color
difference between complex 3D shapes than between 2D objects. The results for 3D and 2D
objects were more similar under only diffuse lighting. Current 3D results had good correlations
with previous ones [Color Res. Appl. 24, 356-368, 1999; J. Opt. Soc. Am. A 36, 789-799, 2019]
using 2D samples with large color differences, meaning that color-difference magnitude had more
effect on perceived color differences than sample shape and lighting. Considering ten modern
color-difference formulas, the best predictions of the current experimental data were found for
CAM02-LCD formula [Color Res. Appl. 31, 320-330, 2006]. For current results, it was also
found that predictions of current color-difference formulas were below average inter-observer
variability, and remarkable improvements were found by adding power corrections [Opt. Express
23, 597-610, 2015].