Illuminant-adaptive color reproduction for mobile display

摘要

This paper proposes an illuminant-adaptive reproduction method using light adaptation and flare conditions for a mobile display. Mobile displays, such as PDAs and cellular phones, are viewed under various lighting conditions. In particular, images displayed in daylight are perceived as quite dark due to the light adaptation of the human visual system, as the luminance of a mobile display is considerably lower than that of an outdoor environment. In addition, flare phenomena decrease the color gamut of a mobile display by increasing the luminance of dark areas and de-saturating the chroma. Therefore, this paper presents an enhancement method composed of lightness enhancement and chroma compensation. First, the ambient light intensity is measured using a lux-sensor, then the flare is calculated based on the reflection ratio of the display device and the ambient light intensity. The relative cone response is nonlinear to the input luminance. This is also changed by the ambient light intensity. Thus, to improve the perceived image, the displayed luminance is enhanced by lightness linearization. In this paper, the image's luminance is transformed by linearization of the response to the input luminance according to the ambient light intensity. Next, the displayed image is compensated according to the physically reduced chroma, resulting from flare phenomena. The reduced chroma value is calculated according to the flare for each intensity. The chroma compensation method to maintain the original image's chroma is applied differently for each hue plane, as the flare affects each hue plane differently. At this time, the enhanced chroma also considers the gamut boundary. Based on experimental observations, the outer luminance-intensity generally ranges from 1,000 lux to 30,000 lux. Thus, in the case of an outdoor environment, i.e. greater than 1,000 lux, this study presents a color reproduction method based on an inverse cone response curve and flare condition. Consequently, the proposed algorithm improves the quality of the perceived image adaptive to an outdoor environment.