AMOLED Grayscale Compensation Using Block-Based Data Compression
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Solution Overview
Problem
The existing methods for generating grayscale compensation data for AMOLED screens require large storage capacity due to the need to calculate and store data for each color channel under different display grayscales, leading to inefficient use of storage resources.
Innovation Solution
A method that converts original grayscale compensation data of pixels into target grayscale compensation data of block regions, reducing storage requirements by processing data in units of block regions, using algorithms like median, mean rounding, or mode algorithms, and optimizing data through virtual grayscale settings and coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If original grayscale compensation data is calculated and stored for each pixel under different display grayscales, then display brightness compensation accuracy is improved, but storage capacity requirement increases
Solution Approach 1:
The patent divides the screen into multiple block regions and processes grayscale compensation data in blocks rather than individual pixels. By segmenting the compensation data into block-level representations, the storage requirement is reduced while maintaining compensation accuracy through block-based processing algorithms.
Solution Approach 2:
The patent merges multiple pixel compensation values within a block region into a single representative compensation value for the entire block. This consolidation approach reduces the total data volume by combining redundant information while preserving the essential compensation function through statistical operations like averaging or median calculation.
2Adaptability or versatility
If grayscale compensation data is stored for all display grayscales, then compensation coverage is improved, but data volume increases
Solution Approach 1:
The patent pre-calculates and stores compensation data only for selected reference grayscales rather than all possible grayscales. This preliminary action allows the system to cover the full grayscale range through interpolation and extrapolation from the reference points, reducing data volume while maintaining comprehensive compensation coverage.
Solution Approach 2:
The patent uses representative compensation data from reference grayscales to generate compensation data for intermediate grayscales through copying and interpolation. Instead of storing unique data for every grayscale level, the system creates approximate copies and variations of reference data to cover the entire grayscale spectrum.
Data Source
AI summary
The present disclosure relates to a grayscale compensation data generation method and apparatus, a device, a storage medium, and a program product. The method includes: acquiring brightness data of a plurality of grayscale images captured under a target shooting brightness, different grayscale images corresponding to different display grayscales; determining original grayscale compensation data of pixels corresponding to different display grayscales respectively according to the brightness data of the plurality of grayscale images; determining storage grayscales from a plurality of display grayscales; converting original grayscale compensation data of pixels corresponding to different storage grayscales respectively into target grayscale compensation data of block regions corresponding to the different storage grayscales, the block regions being regions including a plurality of pixels; and storing the target grayscale compensation data of the block regions corresponding to the different storage grayscales. By use of the method, a storage capacity of grayscale compensation values can be compressed.