Mura Compensation in TFT-LCDs Using Wavelet Compression
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Solution Overview
Problem
Mura defects in TFT-LCDs during mass production cause inefficiencies in detection and compensation times, leading to suboptimal display performance due to varying grayscale values among RGB pixels.
Innovation Solution
An image processing method that calculates average and median grayscale values, determines Mura threshold values, and applies compensation values to pixels, repeating the process to reduce standard deviation, with the results stored in a compressed wavelet format to enhance compensation efficiency and reduce storage space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional Mura detection and compensation methods are used during mass production, then Mura defects can be detected and compensated, but the detection time and compensation time are excessive and the compensation effect is insufficient
Solution Approach 1:
The image is divided into multiple local regions, and Mura detection is performed separately for each region. This allows parallel processing of different areas, reducing overall detection time while maintaining comprehensive coverage of the entire display panel.
Solution Approach 2:
Average grayscale values of all pixels in the image are pre-calculated before local region processing. This preliminary computation establishes a reference baseline that accelerates subsequent Mura detection and compensation operations in each local region.
2Reliability
If additional image processing is applied to compensate for Mura, then display performance can be improved, but processing complexity and time consumption increase
Solution Approach 1:
Mura compensation is applied locally to each region based on its specific characteristics rather than uniformly across the entire image. The compensation parameters are calculated independently for each local region, allowing tailored correction that improves effectiveness while reducing unnecessary processing elsewhere.
Solution Approach 2:
The system iteratively refines Mura compensation by comparing detected Mura values with threshold values and adjusting compensation parameters accordingly. This feedback mechanism ensures progressive improvement of compensation effect while avoiding excessive processing through convergence criteria.
3Measurement precision
If Mura compensation table is stored in full resolution, then compensation precision is maintained, but storage space requirements increase
Solution Approach 1:
Instead of storing the complete high-resolution Mura compensation table, the system stores a compressed version with essential characteristics and reconstructs the full compensation data when needed. This copying approach maintains compensation precision while significantly reducing storage requirements.
Solution Approach 2:
The Mura compensation table is transformed from full-resolution storage to a compressed parameter representation. By changing the storage format from pixel-by-pixel data to compressed parameters that can be reconstructed, the system reduces storage space while preserving the ability to maintain compensation precision.
Data Source
AI summary
An image processing method includes: (a) calculating an average value of grayscale values of each of pixels in a global raw image; (b) calculating Mura threshold values of the grayscale values of all of the pixels in a local raw image; (c) calculating Mura compensation values for each of the pixels of the local raw image in accordance with the Mura threshold value; (d) obtaining updated grayscale values of each of the pixels in the local raw image by adding the grayscale values of each of the pixels in the local raw image and the corresponding Mura compensation values; (e) displaying the updated image; (f) repeating step (b) to (e) for a plurality of times for the updated image with a changed dimension, and calculating a standard deviation.