Abstract
The color reproduction accuracy of digital imaging embedded devices is a key factor of the overall perceived image quality. The human visual system, under most conditions, is able to compensate for the effects of the scene illuminant on the perceived image. Therefore on digital imaging devices, some processes have to be performed across the image generation pipeline in order to obtain an effective color accuracy regardless of the scene illuminant and the sensor response features. In this chapter we describe the most common color processing algorithms performed across the image generation pipeline: white balancing algorithm, which is aimed to compensate the effects of the illuminant power spectral distribution and the color correction process, which compensates the mismatch between the color filters array transmittance and the color response of the human visual system to different wavelengths. The first one, on embedded devices, is usually performed through an image statistical analysis to obtain an estimation of the scene illuminant and is often based on strong assumptions on scene spectral reflectance distribution. The second relies on the characterization of the sensor color filters spectral transmittance. We illustrate the role of such algorithms on the overall perceived color image quality and describe typical methods for white balancing performance and sensor characterization benchmarking. We describe also some additional algorithms which, frequently on consumer devices, can be used to improve the visual appearance of common colored objects (e.g., skin tones, vegetation and sky).