Photosensor Brightness Detection Accuracy via Segmented Calibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing display technologies face challenges with low accuracy, inter-chip differences, and 'zero point' drift in brightness detection due to the semiconductor characteristics of photosensitive sensors.
Innovation Solution
A brightness detection method that involves testing each display module separately to obtain a unique brightness algorithm formula for the photosensitive sensor, using segmented curve fitting and polynomial algorithms to improve accuracy across various brightness intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single brightness algorithm formula is used for all photosensitive sensors, then device complexity is reduced, but measurement precision deteriorates due to inter-chip differences
Solution Approach 1:
The patent segments the brightness detection process by dividing it into two parts: a universal algorithm framework and sensor-specific calibration parameters. The brightness algorithm is segmented into standard processing steps and adaptive calibration coefficients. During production, each photosensitive sensor undergoes calibration to obtain unique parameters (such as sensitivity coefficients and offset values) that are stored in a lookup table. This allows the system to use a standardized algorithm structure while adapting to individual sensor characteristics, thereby maintaining low device complexity while improving measurement precision across different chips.
2Measurement precision
If segmented curve fitting is performed for low, middle, and high brightness intervals separately, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides the brightness detection range into three distinct intervals: low brightness (0-100 cd/m²), middle brightness (100-1000 cd/m²), and high brightness (1000-5000 cd/m²). Each interval has its own calibration curve and algorithm parameters optimized for that specific range. This segmentation allows the system to achieve high measurement precision in each interval while using simplified linear or quadratic fitting models rather than complex global models, thus balancing improved accuracy with controlled algorithm complexity.
Solution Approach 2:
The patent applies local quality by using different algorithm parameters and calibration curves tailored to specific brightness intervals. Each interval (low, middle, high) has locally optimized parameters such as sensitivity coefficients and curve fitting coefficients that are specific to that brightness range. This local optimization ensures high measurement precision for each interval without requiring a single complex algorithm to handle all ranges, thereby improving accuracy while maintaining manageable device complexity.
3Measurement precision
If separate calibration is performed for each display module, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent implements preliminary calibration action by performing brightness parameter calibration for each photosensitive sensor during the display module production process. The calibration is conducted using standardized procedures and equipment on the production line, and the obtained parameters are immediately stored in the module's memory or lookup tables. This preliminary action ensures that each module is pre-calibrated before leaving the factory, achieving high measurement precision while maintaining production efficiency through automated, streamlined calibration processes that are integrated into the existing production flow.
Solution Approach 2:
The patent uses parameter changes by calibrating specific parameters (such as sensitivity coefficients, offset values, and curve fitting coefficients) for each display module during production. Instead of recalibrating the entire system or performing complex adjustments, the method focuses on measuring and storing key parameters that characterize each sensor's response. These parameters are then used by the brightness algorithm to achieve high detection accuracy. This approach enables quick, efficient calibration that maintains productivity while improving precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method significantly enhances brightness detection accuracy by addressing inter-chip differences and 'zero point' drift, ensuring consistent performance across low, middle, and high-brightness intervals.
Implementation Method 1
handheld devices such as tablet computers and mobile phones are equipped with a photosensitive sensor (sensor). The photosensitive sensor can automatically adjust the screen brightness
Data Source
AI summary
The application provides a brightness detection method, a computer device and a readable medium, where the brightness detection method includes: using each of display modules in a display module production line as a test module separately, where the test module is provided with a photosensitive sensor; obtaining a brightness algorithm formula of the photosensitive sensor of each of the test module; and performing, according to the brightness algorithm formula, ambient light detection by the photosensitive sensor.