We developed a quantitative evaluation method for luminance and color uniformity on a display screen. In this paper, we report the analysis result of a viewer perception of luminance and color uniformity. In experiments, observers subjectively evaluated Mura images which were showed on the light emitting diode (LED) backlight screen by adjusting the luminance of each LED. We measured the luminance and color distributions of the Mura images by a 2D colorimeter, then, the measured data was converted into S-CIELAB. In S-CIELAB calculations, two dimensional MTF (Modulation Transfer Function) of human eye were used in which anisotropic properties of the spatial frequency response of human vision were considered. Some indexes for a quantitative evaluation model were extracted by the image processing. The significant indexes were determined by the multiple regression analysis to quantify the degree of uniformity of the backlight screen. The luminance uniformity evaluation model and color uniformity evaluation model were derived from this analysis independently. In addition, by integrating both of these models we derived a quantitative evaluation model for luminance and color unevenness simultaneously existing on the screen.

This report describes a quantification method for luminance non-uniformity of a large LED backlight. In experiments described herein, participants subjectively evaluated artificial indistinct Mura images that simulated luminance non-uniformity of an LED backlight. We measured the luminance distribution of the Mura images. Then, the measured luminance distribution was converted into S-CIELAB, in which anisotropic properties of the spatial frequency response of human vision were considered. Subsequently, some indexes for the quantification model were extracted. We conducted multiple regression analyses using the subjective evaluation value and the index values obtained from measured luminance of Mura image. We proposed a quantification model consisting of four indexes: high and low luminance area, number of Mura edges, sum of Mura edge areas, and maximum luminance difference.

An analytical approach using human perception has been applied to the evaluation of the front-of-screen (FOS) quality of liquid crystal displays (LCDs), particularly regarding the regions of luminance nonuniformity called "muras." The accurate and consistent inspection of muras is extremely difficult because muras have various shapes and sizes as well as contrasts. And inspection results tend to depend on inspectors during the LCD manufacturing process. To determine the quantitative scale that shows the evaluation results of mura matching human perceptions, first, we conducted a perception test and clarified the "just noticeable difference" (JND) contrast according to the type of mura. Second, the relationship between the JND contrast of mura and background luminance was investigated. Finally, we proposed a quantitative scale of mura level on the basis of the JND contrasts at various background luminances. In this paper, we describe our research on human perception of muras at various background luminances and an approach to determining the quantitative scale of visible muras.