Screen Light Leakage Detection via Pixel Clustering
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Solution Overview
Problem
Existing methods for detecting light leakage from screens in electronic devices, such as mobile phones, are inefficient due to high time and power consumption, making them unsuitable for practical application scenarios.
Innovation Solution
A method that involves acquiring brightness and grayscale values of pixel points within a preset display region, clustering them into sets based on grayscale value intervals, and calculating light leakage values using weighted summation and neural network processing to reduce computational load and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to detect light leakage of the screen, then measurement precision can be achieved, but time consumption and power consumption increase significantly
Solution Approach 1:
The screen display region is divided into multiple pixel point sets based on grayscale value intervals. Instead of processing all pixel points individually, the method segments them into groups where pixel points within each set share similar grayscale characteristics. This segmentation reduces the computational complexity from processing N individual pixel points to processing M pixel point sets where M << N, thereby significantly reducing detection time while maintaining measurement precision through representative sampling.
Solution Approach 2:
The method performs light leakage detection on a representative subset of pixel points rather than all pixel points. By selecting pixel points that represent different grayscale levels and spatial distributions, the system achieves sufficient detection accuracy with partial sampling. This partial action approach reduces computational load and time consumption while still providing reliable light leakage measurement for the entire screen.
2Measurement precision
If traditional methods are used to detect light leakage of the screen, then measurement precision can be achieved, but power consumption increases significantly
Solution Approach 1:
By segmenting pixel points into grayscale-based groups, the method reduces the number of computational operations required for light leakage detection. Fewer calculations directly translate to lower power consumption in the processor, while the segmentation strategy ensures that representative pixel points are still processed to maintain detection accuracy.
Solution Approach 2:
The method changes the processing parameter from individual pixel points to pixel point sets characterized by fused grayscale values. This parameter change reduces computational intensity and consequently power consumption. The fused grayscale value serves as a representative parameter that captures the essential characteristics of multiple pixel points, enabling accurate detection with reduced energy expenditure.
3Measurement precision
If pixel points are processed individually for light leakage detection, then accuracy is maintained, but device complexity increases
Solution Approach 1:
The computational task is segmented by grouping pixel points into sets based on grayscale values. This segmentation transforms a complex O(N) processing task into a simpler O(M) task where M is the number of pixel point sets. The segmentation approach maintains accuracy by ensuring representative coverage of different grayscale levels while significantly reducing computational complexity.
Solution Approach 2:
Multiple pixel points with similar grayscale characteristics are merged into single pixel point sets. The grayscale values of individual pixel points are fused into a representative value for each set. This merging operation reduces the number of processing units from N individual pixel points to M pixel point sets, simplifying the computational model and reducing device complexity while preserving detection accuracy through representative sampling.
Data Source
AI summary
Embodiments of the present disclosure provide a method for detecting light leakage of a screen, a method for detecting ambient light, and an apparatus for detecting ambient light. The method for detecting light leakage of a screen includes: acquiring a brightness value of the screen and grayscale values of pixel points within a preset display region of the screen; clustering the pixel points based on a plurality of preset grayscale value intervals and the grayscale values of the pixel points, to obtain a plurality of pixel point sets; obtaining light leakage values corresponding to the pixel point sets based on the brightness value and fused grayscale values corresponding to the pixel point sets; and obtaining a light leakage value of the screen based on the light leakage values corresponding to the pixel point sets.