Display Driver Predicts On-Pixel Ratio for Luminance Stability
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Solution Overview
Problem
Existing display devices face challenges in reducing power consumption and preventing extreme luminance changes due to delays in on-pixel ratio calculations, leading to potential display panel specification exceedances.
Innovation Solution
A display device that predicts the on-pixel ratio of a current frame using an artificial neural network model based on input image data from a previous frame, determining a first adjustment value to adjust the luminance accordingly, and storing these values in a look-up table for efficient luminance adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the current automatic current limitation method adjusts the luminance in the next frame after calculating the on-pixel ratio of one frame, then power consumption can be reduced, but an extreme change in the luminance may be recognized
Solution Approach 1:
The patent applies preliminary action by predicting the on-pixel ratio of the current frame in advance using a neural network model trained on previous frame data. This prediction is performed before the current frame is displayed, allowing the luminance adjustment to be prepared beforehand. The adjustment value is calculated based on the predicted on-pixel ratio, ensuring that the luminance change is smooth and continuous rather than abrupt, thus resolving the contradiction between power consumption reduction and luminance stability.
2Loss of time
If the on-pixel ratio calculation is performed with a delay, then processing time is reduced, but extreme luminance changes occur that may exceed display panel specifications
Solution Approach 1:
The patent performs the on-pixel ratio prediction in advance for the current frame using a neural network model that has been trained on historical data from previous frames. This preliminary prediction allows the system to prepare the luminance adjustment value before the current frame needs to be displayed, eliminating delays and ensuring that the display panel operates within its specifications without extreme luminance changes.
Solution Approach 2:
The patent uses a neural network model that learns from and copies patterns in the on-pixel ratio data from previous frames. By training the model on historical data, it creates a predictive copy of the on-pixel ratio behavior, allowing accurate predictions without real-time calculation delays, thus maintaining both speed and reliability.
3Illumination intensity
If luminance adjustment is based on predicted on-pixel ratio without current frame input data, then extreme luminance changes are prevented, but prediction accuracy must be maintained
Solution Approach 1:
The patent implements a feedback mechanism where the neural network model is continuously trained and refined using actual on-pixel ratio data from displayed frames. The model compares its predictions with actual values and adjusts its parameters to minimize errors. This feedback loop ensures that the prediction accuracy is maintained and improved over time, allowing the system to reliably predict on-pixel ratios without needing current frame input data, thus maintaining luminance stability.
Data Source
AI summary
A display device includes a display panel including pixels, and a display panel driver configured to drive the display panel. The display panel driver is configured to determine a predicted on-pixel ratio of a current frame based on an artificial neural network model and input image data of a previous frame, determine a first adjustment value based on the predicted on-pixel ratio, and adjust a luminance of the current frame based on the first adjustment value.