Portable LCD Image Quality: Effects of Surround Luminance

Abstract

Liquid crystal display (LCD) has become a major technology in a variety of display application markets from small sized portable displays to large sized televisions. Portable LCD devices such as smart phones and mobile phones are used in a diverse range of viewing conditions. We usually experience images on a mobile phone with a huge loss in contrast under bright outdoor viewing conditions; thus, viewing condition parameters such as surround effects, correlated colour temperature and ambient lighting have become of significant importance. (Katoh et al., 1998; Moroney et al., 2002) Recently, auxiliary attributes determining the mobile imaging were examined and the surround luminance and ambient illumination effects were considered as the first major factor. (Li et al., 2008) Surround and ambient lighting effects on colour appearance modelling have been extensively studied to understand the nature of colour perception under various ambient illumination levels (Liu & Fairchild, 2004, 2007; Choi et al., 2007; Park et al., 2007); thus, this study intends to figure out characteristics of the human visual system (HVS) in spatial frequency domain by means of analysing the contrast discrimination ability of HVS. In consequence, we propose an image quality evaluation method and a robust image enhancement filter based on the measured contrast sensitivity data of human observers under various surround luminance levels. The former is to quantify the observed trend between surround luminance and contrast sensitivity and to propose an image quality evaluation method that is adaptive to both surround luminance and spatial frequency of a given stimulus. The non-linear behaviour of the HVS was taken into account by using contrast sensitivity function (CSF). This model can be defined as the square root integration of multiplication between display modulation transfer function (MTF) and CSF. It is assumed that image quality can be determined by considering the MTF of an imaging system and the CSF of human observers. The CSF term in the original SQRI model (Barten, 1990) is replaced by the surround adaptive CSF quantified in this study and it is divided by the Fourier transform of a given stimulus. The latter is a robust image enhancement filter which compensates for the effects of surround luminance on our contrast perceiving mechanism. Precisely, the surround luminance adaptive CSF is used as a guide for determination of the adaptive enhancement gain in the proposed algorithm.