How Good Are RGB Cameras Retrieving Colors of Natural Scenes and Paintings?-A Study Based on Hyperspectral Imaging.

José A R Monteiro,Liliana Cardeira,Andrea Casini,Costanza Cucci,Taisei Kondo,Shigeki Nakauchi,Marcello Picollo,Sérgio Miguel Cardoso Nascimento,João M M Linhares,Lorenzo Stefani,Ana Bailão

doi:10.3390/s20216242

Abstract

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 ΔE*ab and CIEDE2000), Jzazbz, and iCAM06. In CIELAB the most frequent error (using ΔE*ab) 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.

Highlights

Digital color cameras acquire images by sampling the spatial and spectral information available, much like the human eye does [1]
To what extent typical cameras are faithful to the real colors of complex natural scenarios? much work has been done characterizing fidelity with simple stimuli [6,7,8,14,15,16] it is unclear how they perform with complex imagery, such as complex natural scenes. We address this question by using hyperspectral images of natural scenes and paintings, which contains the contexts and information required for the analysis presented.The spectral reflectance data in the hyperspectral images was converted into colors as perceived by a human observer (OBS)
Comparison between the actual colors and those produced by RGB cameras show that the most

Summary

Introduction

Digital color cameras acquire images by sampling the spatial and spectral information available, much like the human eye does [1]. The acquisition process compresses the spectral information reflected from the objects into three components, discarding most of the spectral information initially available. The entire process produces colors that are device-dependent, different from camera to camera, and do not map linearly to the device-independent tristimulus values, Sensors 2020, 20, 6242; doi:10.3390/s20216242 www.mdpi.com/journal/sensors. Hyperspectral images acquire the spatial information without the chromatic compression found in digital cameras, maintaining the spectral properties of the light signal, information relevant to research in vision [3]. Some of the colors may be made metameric by the process [9]

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