Display Gamma Correction with Deep Learning Panel Models
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Solution Overview
Problem
Gamma correction in display devices is affected by factors other than grayscale level, leading to inconsistent image quality due to variations in luminance.
Innovation Solution
A method employing deep learning to generate a representative panel model based on luminance factors, followed by transfer learning to create a panel model for the display device, determining grayscale voltage to maintain specific gamma characteristics despite luminance variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If grayscale voltage is predetermined based on grayscale level to achieve specific gamma characteristic, then gamma correction is improved, but luminance accuracy deteriorates due to other affecting factors
Solution Approach 1:
The patent changes the parameters for determining grayscale voltage from solely relying on grayscale level to incorporating multiple luminance factors (frame frequency, on-duty ratio, power supply voltage, initialization voltage). This multi-parameter approach allows the system to compensate for variations in luminance while maintaining gamma correction accuracy, resolving the contradiction between gamma correction precision and luminance measurement accuracy.
2Ease of operation
If traditional gamma correction method is used, then implementation simplicity is maintained, but image quality consistency deteriorates under varying luminance conditions
Solution Approach 1:
The patent introduces luminance factors as intermediary parameters that mediate between the grayscale level and the final grayscale voltage. These factors (frame frequency, on-duty ratio, power supply voltage, initialization voltage) act as mediators that account for additional influences on luminance, enabling the system to maintain image quality consistency across varying conditions while building upon the existing simple gamma correction framework.
3Reliability
If multiple luminance factors are considered in gamma correction, then image quality consistency is improved, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-determining the relationships between luminance factors and grayscale voltage through controlled measurements and data collection before actual display operation. The system pre-calculates and stores correction data for different combinations of luminance factors, allowing the complex multi-factor gamma correction to be executed efficiently during operation without real-time complex computations, thus managing system complexity while maintaining image quality consistency.
Data Source
AI summary
A method of correcting gamma includes generating a representative panel model by performing a deep learning based on luminance factors and a representative display panel, generating a panel model by performing a transfer learning based on the representative panel model and a display panel, and determining a grayscale voltage for the display panel based on the panel model.