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How the Mura Effect Impacts LCD Quality: A Professional Guide

calendar_month Jan 12, 2026
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By Emily Li Lead Display Quality Engineer at RisingStar

About the Author

Emily Li brings over 8 years of experience in display manufacturing, defect analysis, and quality control. At RisingStar, she leads mura detection and calibration projects, utilizing both human expertise and automated imaging systems to ensure the integrity of industrial-grade LCD panels.


Introduction

In the display industry, the term "Mura" (a Japanese word meaning "unevenness" or "blemish") refers to non-uniformity in brightness, color, or texture across a panel. These defects manifest as patches, streaks, or "clouding" that degrade the visual experience.

For an LCD manufacturer, shipping a display with a mura problem is a disaster. It is particularly critical for high-reliability applications, such as  outdoor digital signage  and  industrial high-brightness monitors , where consistency is paramount.


What Causes the Mura Effect?

Mura is rarely the result of a single factor. It generally stems from subtle imperfections during the manufacturing process or physical stress:

  • Layer Inconsistency: Variations in the thickness or density of liquid crystal layers.

  • Backlight Non-uniformity: Inconsistencies in LEDs or light diffuser plates.

  • Material Impurities: Contaminants or particles trapped within the panel layers.

  • Mechanical Stress: Thermal expansion or physical pressure during the assembly of the panel into its housing.


    Common Types of Mura Problems

    Understanding the visual signature of mura helps in identifying the root cause in the production line:

    Real-World Testing: The RisingStar Protocol

    At RisingStar, we perform a rigorous multi-stage testing process on panels sourced from Tier-1 OEMs like LG, BOE, and AUO. We test upon arrival (Open-Cell) and again after the 72-hour aging test post-assembly.

    1. Visual Inspection

    Trained engineers examine the display in a dark room. While subjective, this is the most intuitive method for catching obvious defects.

    2. Gray-Level & Luminance Analysis

    We display specific gray-level patterns to measure luminance across different zones. Any deviation in "nits" beyond a specific tolerance indicates a mura defect.

    3. Image Subtraction & JND Mapping

    Using tools from Radiant Vision Systems, we use imaging colorimeters to create Just Noticeable Difference (JND) maps. This objectively quantifies defects that the human eye might miss but a customer might notice.

    4. Machine Learning Detection

    We are increasingly deploying CNN (Convolutional Neural Networks) to automate detection. These algorithms can classify mura types with over 90% accuracy, ensuring objective quality control.


    Solutions and Mitigation Strategies

    To ensure only A+ Grade panels reach our customers, we employ the following strategies:

    • Manufacturing Optimization: Refining liquid crystal alignment and improving the precision of backlight assembly.

    • Compensation Algorithms: Developing software that dynamically adjusts the output of specific pixels to "mask" or balance out non-uniformity.

    • Uniformity Enhancement Films: Applying specialized optical films that help diffuse light more evenly across the surface.

    • Calibration: Fine-tuning brightness, contrast, and gamma settings during the final assembly stage to compensate for panel-specific inconsistencies.


      Conclusion

      Mura is a complex challenge, but it is not insurmountable. By blending rigorous testing, expert human analysis, and emerging AI technologies, manufacturers can ensure high-quality display performance. At  RisingStar , our commitment to double-testing and 72-hour aging ensures that every screen meets the demanding standards of industrial and outdoor applications.

         #Luminance uniformity #JND (Just Noticeable Difference)   #   Backlight bleeding ,    A+ grade LCD panel 

      For technical inquiries or custom mura testing solutions, please contact ai@risinglcd.com or leave a comment below.

      Disclaimer: All testing data reflect in-factory conditions. Actual results may vary based on environmental factors and specific usage scenarios.

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