Paper
29 March 1996 LLAB model for color appearance and color difference evaluation
Author Affiliations +
Proceedings Volume 2658, Color Imaging: Device-Independent Color, Color Hard Copy, and Graphic Arts; (1996) https://doi.org/10.1117/12.236975
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States
Abstract
An ideal system of colorimetry should provide measures agreeing to what we see in three respects: color specification, difference and appearance. A successful method to quantify these measures depends upon the reliability of psychophysical experimental data. These data sets have been accumulated and were used to derive the LLAB model. The model includes two parts: a chromatic adaptation transform and a uniform color space. Tristimulus values under a particular set of illuminant/observer conditions are transformed to those of D65/2 degree(s) conditions via a chromatic adaptation transform. A modified version of CIELAB is then used to calculate six perceived attributes: lightness (LL), redness-greenness (AL), yellowness-blueness (BL), colorfulness (CL), hue angle (hL) and hue composition (HL). The model gives similar degree of prediction in comparison with the other state of the art models using the accumulated data sets. The LLAB model demonstrates that it is possible to achieve a system, which provides precise measures to quantify color match, difference and appearance.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ming Ronnier Luo "LLAB model for color appearance and color difference evaluation", Proc. SPIE 2658, Color Imaging: Device-Independent Color, Color Hard Copy, and Graphic Arts, (29 March 1996); https://doi.org/10.1117/12.236975
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data modeling

Colorimetry

Performance modeling

Visual process modeling

Visualization

Image processing

CRTs

Back to Top