Assessing RGB Color Reliability via Simultaneous Comparison with Hyperspectral Data on Pantone® Fabrics

Accurate color property measurements are critical for advancing artificial vision in re-al-time industrial applications. RGB imaging remains highly applicable and widely used due to its practicality, accessibility, and high spatial resolution. However, significant un-certainties in extracting chromatic information highlight the need to define when conven-tional digital images can reliably provide accurate color data. This work simultaneously compares six chromatic properties across 700 Pantone® TCX fabric samples, using opti-cal data acquired simultaneously from both hyperspectral (HSI) and digital (RGB) camer-as. The results indicate that the accurate interpretation of optical information from RGB (sRGB and REC2020) images is significantly influenced by lightness (L*) values. Samples with bright and unsaturated colors (L* > 50) reach ratio-to-performance-deviation (RPD) values above 2.5 for four properties (L*, a*, b* H), indicating a high correlation between HSI and RGB information. Direct color difference comparisons (∆E) between HSI and RGB images yield values exceeding 5.5 for red-yellow-green samples and up to 9.0 for blue and purple tones. However, when relative color differences (∆E′) are calculated using a specific reference, the values drop significantly falling below 2.0 for light-colored samples (L* > 50). These results confirm that RGB imagery achieves reliable color consistency when evalu-ated against a practical reference.