P‐57: A Novel Compression Algorithm using Machine Learning for Mura Compensation of OLED Panel

In the modern display panel manufacturing industry, the Mura problem that occurs during initial shipment has a significant impact on improving image quality. The purpose of this paper is to effectively solve these Mura defects while reducing additional production costs. We propose an innovative meth...

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Bibliographic Details
Published in:SID International Symposium Digest of technical papers 2024-06, Vol.55 (1), p.1593-1596
Main Authors: Son, Chang-Hoon, Kim, Chan-Yung, Shin, Hyeon-Woon, Lim, Ho-Min, Lee, Ji-Won
Format: Article
Language:English
Subjects:
Algorithms
Compensation
Compression
Compression ratio
Data compression
De‐Mura
Image compression
Image degradation
Image quality
Machine learning
OLED
Production costs
Surface Fitting
Vector Quantization
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Summary:In the modern display panel manufacturing industry, the Mura problem that occurs during initial shipment has a significant impact on improving image quality. The purpose of this paper is to effectively solve these Mura defects while reducing additional production costs. We propose an innovative method of storing and compressing the De‐Mura compensation data of the display panel in external memory. Two‐dimensional compensation data compression based on machine learning using surface fitting and vector quantization is presented as an effective response to the Mura problem. This approach, which achieves up to 8:1 compression ratio while minimizing image quality degradation through vector quantization, is expected to provide economic benefits to manufacturers. The paper also introduces a new surface prediction method that replaces the existing DC‐shift and uses residual vector quantization, which can reduce the implementation complexity of vector quantization. Through experimental results, this study demonstrates that the compensated data compression technique yields excellent results in post‐compression restoration performance.
ISSN:0097-966X
2168-0159
DOI:10.1002/sdtp.17865