One of the visual problems hardest to recognize in liquid crystal displays (LCDs) is an area of non-uniform brightness called a mura. The accurate and consistent detection of a low-contrast mura is extremely difficult because the boundary between the regional mura and the background is indistinct. This paper presents a novel method for detection and quantitative measurement of low-contrast mura. Compared with some wavelet approaches, the multiple resolution analysis method based on the Symmetric Selesnick multiwavelet has advantages for practical use.
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 nonuniform brightness called mura. The accurate and consistent detection of the mura is extremely difficult because there are various shapes and sizes of mura and the inspection results tend to depend on the operators. We conducted a study on the quantitative evaluation of mura 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 charge-coupled-device camera and a personal computer system with an image processor board. This system can successfully identify and evaluate mura. The algorithm was developed from research on visual analysis and human perception. We converted the front-of-screen images from the LCDs into distributions of luminance information, and the mura regions were distinguished from the background area using our novel algorithm. Our identification method can also distinguish between the muras caused by flaws in the LCD cells and the intentionally designed nonuniform luminance distribution of the backlight.
An analytical approach using human perception has been introduced to evaluate the front-of-screen (FOS) quality of liquid crystal displays (LCDs), in particular regarding the regions of the liminance non-uniformity called "mura". The word "mura" is a Japanese term similar to "blemish" and has been adopted in English to provide a precise name for certain imperfections of the display pixel matrix surfaces that are visible when the display is in active use. The accurate and consistent detection of the mura is extremely difficult because there are various shapes and sizes of mura and the inspection results tend to depend on the inspectors during the LDC manufacturing process.
We conducted a study on the quantitative evaluation of mura based on visual analysis, intending to clarify the detection method and create an automated mura inspection process. We developed an algorithm to extract and evaluate mura by using 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 mura. 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.
In order to further match the evaluation results of mura to human perceptions, we conducted a perception test with some expert inspectors by using pseudo mura and this approach led to categorizing "just noticeable differences" according to the varieties of mura. This paper describes the research in human perception and the approach adapting the intrinsic rules of sensory analysis to the quantitative evaluation of mura.
Based on sensory analysis, quantitative evaluation method of the luminance non-uniformity, or 'mura', of liquid crystal displays (LCDs) was investigated. We conducted a perception test by using pseudo mura and this approach led to 'just noticeable differences' according to the various sizes of muras, intending to clarify the detection method and create an automated mura inspection process. The quality level of a mura can be described as a function between the mura area and the contrast, using the minimum perceivable contrast, or the 'just noticeable difference' (JND) contrast, at that mura size. We developed the detection method by using a hardware system based on a commercially available CCD camera and a PC and ensured that the mura regions were distinguished from the background area even with the JND contrast. This paper describes the research in human perception and the approach to adapt the intrinsic rule of sensory analysis to the quantitative evaluation of mura.
The visual performance of liquid crystal displays (LCDs) has been usually inspected and evaluated by sensory analysis at the manufacturing process. One of the most indistinct visual problems is low-contrast non-uniform brightness region 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 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.
The front-of-screen quality as the visual performance of thin- film transistor liquid crystal displays were investigated using the original algorithm for uniformity analysis. Extraction and evaluation of 'mura,' which was low-contrast and non-uniform brightness region, was successfully demonstrated using commercial-available test system with our algorithm. The algorithm was led out from researching of sensory analysis and human perception. The sensitivity of the technique was demonstrated by a detectable change at even the small level which was perceptible by experienced manufacturing inspectors. The evaluation and judgement for 'mura' using our technique closely matched the results from the experienced inspectors.
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