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首页> 外文会议>Conference on Algorithms and Systems for Optical Information Processing Ⅴ Jul 31-Aug 2, 2001, San Diego, USA >Extraction and evaluation of mura images in liquid crystal displays
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Extraction and evaluation of mura images in liquid crystal displays

机译：液晶显示器中MURA图像的提取和评估

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摘要

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.
机译：液晶显示器（LCD）的视觉性能通常在制造过程中通过视觉检查进行评估。最难识别的视觉问题之一是称为muras的低对比度和亮度不均匀区域。由于存在各种形状和尺寸的色斑，并且要检查的结果往往取决于操作员，因此很难准确，一致地检测色斑。我们基于目视分析进行了对muras定量评估的研究，旨在阐明检测方法并创建自动化的mura检查过程。我们基于市售的CCD相机和带有图像处理器板的PC开发了一种算法和硬件系统。该系统可以成功识别和评估muras。该算法是根据视觉分析和人类感知的研究开发的。我们将来自LCD的屏幕前（FOS）图像转换为亮度信息的分布，并使用我们的新颖算法将mura区域与背景区域区分开。这种方法还导致出现在面板中的muras类别的加权函数。我们的识别方法还可以区分由LCD单元中的缺陷引起的色斑和故意设计的背光的不均匀亮度分布。

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