Display Panel Grayscale Correction for Mura Defect Compensation
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Solution Overview
Problem
Conventional liquid crystal display panels face challenges in accurately detecting and compensating for Mura defects due to their cyclical, random, and low contrast nature, leading to inconsistent results and reduced productivity in quality assurance tests.
Innovation Solution
A method and apparatus for driving a display panel that generates corrected grayscale data using a grayscale correction value from a reference pixel, determining luminance and gamma curves, and applying these corrections to compensate for Mura defects, thereby ensuring a uniform image display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual inspection is used to detect Mura defects, then human judgment can identify display pattern stains, but the process is time-consuming and provides inconsistent results
Solution Approach 1:
The patent replaces manual visual inspection with an automated image processing system that captures display images, converts them to grayscale, and applies algorithms to detect Mura defects. This substitution of mechanical/manual processes with automated systems resolves the contradiction by providing both high detection accuracy through systematic image analysis and high productivity through rapid automated processing.
Solution Approach 2:
The patent creates a grayscale copy of the color display image to enhance contrast and facilitate automated defect detection. By converting the display image to grayscale and applying thresholding algorithms, the system can accurately detect Mura defects while processing images rapidly, thus achieving both high measurement precision and productivity.
2Reliability
If manual inspection is used, then detailed pattern analysis is possible, but results are inconsistent across inspectors and productivity is reduced
Solution Approach 1:
The patent replaces subjective human judgment with objective image processing algorithms that consistently apply the same detection criteria to all images. This automation ensures reliable and consistent results across all inspections while maintaining high throughput, directly resolving the contradiction between reliability and productivity.
Solution Approach 2:
The patent transforms the inspection process by changing parameters such as converting color images to grayscale, applying threshold values, and using luminance calculations. These parameter transformations enable consistent automated detection that is both reliable and efficient, overcoming the limitations of manual inspection.
3Manufacturing precision
If grayscale correction is applied to compensate for Mura defects, then display uniformity is improved, but the complexity of the driving method increases
Solution Approach 1:
The patent applies grayscale correction values to the display panel before normal operation to pre-compensate for Mura defects. By determining correction values in advance and applying them through the driving method, the system achieves uniform display output without adding complex real-time processing during normal operation, thus improving manufacturing precision while managing device complexity.
Solution Approach 2:
The patent applies different grayscale correction values to different regions or pixels of the display panel based on their specific Mura defect characteristics. This localized correction approach improves display uniformity by addressing specific defect areas while keeping the overall driving method relatively simple through region-based processing.
Data Source
AI summary
A method of driving a display panel includes generating corrected grayscale data utilizing a grayscale correction value of a reference pixel including m×n pixels, “m” and “n” being natural numbers greater than zero, and driving M×N pixels of the display panel based on the corrected grayscale data, “M” and “N” being natural numbers greater than zero. “M” is greater than “m” and “N” is greater than “n.”