Demura Compensation Compression Using Adaptive Prediction Modes
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Solution Overview
Problem
Current compression methods for Demura compensation values in OLED display screens are inefficient due to varying data characteristics across different panels, leading to suboptimal storage resource utilization and increased chip costs.
Innovation Solution
A method and system that utilize a spatial domain sampling model and prediction mode overall model to perform down-sampling, numerical processing, data reconstruction, and syntactic encoding to select an optimal prediction mode for compressing Demura compensation values, adapting to specific panel characteristics and improving generalization performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional compression methods are used for Demura compensation values, then the compression process is simple, but the compression efficiency is low and storage resource utilization is suboptimal
Solution Approach 1:
The patent divides the compression process into multiple independent modules: spatial domain sampling module, prediction mode overall model, data reconstruction module, and syntactic encoding module. Each module handles a specific aspect of the compression task, allowing for optimized processing at each stage while maintaining overall system efficiency.
Solution Approach 2:
The patent implements dynamic mode selection where the system automatically selects the optimal prediction mode (from multiple candidate modes including mean value prediction, moving average prediction, knot prediction, cluster prediction, linear difference prediction, quadratic interpolation prediction, and multi-data mode fitting prediction) based on the actual characteristics of the compensation data. This dynamic adaptation improves compression efficiency without requiring a fixed complex structure.
2Adaptability or versatility
If a fixed compression method is used, then the implementation is straightforward, but it cannot adapt to varying data characteristics across different panels
Solution Approach 1:
The patent changes the parameters of the prediction model based on the input data characteristics. The system evaluates multiple prediction modes and selects the one that best fits the current panel's data patterns. This parameter adaptation allows the same compression framework to effectively handle diverse panel characteristics without requiring manual reconfiguration.
Solution Approach 2:
The patent incorporates feedback mechanisms where the compression system evaluates the performance of different prediction modes on the actual panel data and uses this feedback to select the optimal mode. The system continuously adapts based on the reconstructed value set and syntactic element code set, ensuring optimal performance for each specific panel while maintaining automated operation.
3Manufacturing precision
If comprehensive data processing is performed, then the compression effectiveness is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial processing by performing down-sampling in the spatial domain and using selective prediction modes that process only the essential features of the data. The syntactic encoding further compresses the representation by focusing on the most significant elements. This partial action approach achieves effective compression without requiring exhaustive processing of all data points.
Solution Approach 2:
The patent extracts key characteristics from the compensation data through spatial domain sampling and prediction mode analysis. By identifying and processing only the essential features (mode characteristic values) rather than all raw data, the system achieves effective compression with reduced processing time and computational resources.
Data Source
AI summary
A method for compressing Demura compensation value including: acquiring original compensation values of a target panel; inputting the original compensation values into a spatial domain sampling model, performing a down-sampling to obtain spatial domain sampling values, inputting the original compensation values into a prediction mode overall model, and performing a numerical processing to obtain a plurality of mode characteristic values; performing a data reconstruction on the spatial sampling values and the plurality of mode characteristic values to obtain a reconstructed value set; performing a syntactic encoding on the spatial sampling values and the plurality of mode characteristic values to obtain a syntactic element code set; performing a mode selection according to the reconstructed value set and the syntactic element code set to obtain an optimal prediction mode; and acquiring and outputting a syntactic element code corresponding to the optimal prediction mode to obtain a Demura compensation value compression code.