Extraction and evaluation of mura images in liquid crystal displays

The visual performance of liquid crystal displays (LCDs) has usually been evaluated by visual inspection during the manufacturing process. One of the visual problems hardest to recognize are regions of low-contrast and non-uniform brightness called muras. The accurate and consistent detection of the muras is extremely difficult because there are various shapes and sizes of muras and the inspection results tend to depend on the operators. We conducted a study on the quantitative evaluation of muras based on visual analysis, intending to clarify the detection method and create an automated mura inspection process. We developed an algorithm and a hardware system based on a commercially available CCD camera and a PC with an image processor board. This system can successfully identify and evaluate muras. The algorithm was developed from research on visual analysis and human perception. We converted the front-of-screen (FOS) images from the LCDs into distributions of luminance information, and the mura regions were distinguished from the background area using our novel algorithm. This approach also led to a weighting function for the categories of muras that appear in the panels. Our identification method can also distinguish between the muras caused by flaws in the LCD cells and the intentionally designed non-uniform luminance distribution of the backlight.