Temporal Dithering for Accurate Display Stress Accumulation
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Solution Overview
Problem
Display devices face challenges in accurately tracking and storing accumulative stress data, such as luminance and color stress, due to limited memory constraints, which can lead to image burn-in and reduced display panel longevity, especially with increasing resolutions and usage.
Innovation Solution
A temporal dithering technique is employed to compress accumulative data by applying carefully chosen dithering offsets before quantization and accumulation, allowing for efficient storage of stress data in limited memory while maintaining accuracy, using a processor to manage scale factors and dither increments to minimize quantization error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If accumulative data is stored in full precision, then measurement precision is improved, but memory capacity is exceeded
Solution Approach 1:
The patent applies quantization by changing the precision parameter of accumulative data from full precision to reduced precision (e.g., from 32-bit to 16-bit). This parameter change allows the data to fit within available memory capacity while maintaining sufficient accuracy for stress tracking applications. The quantization process systematically reduces the number of bits required to represent each data point, directly resolving the contradiction between precision and storage capacity.
Solution Approach 2:
The patent extracts and discards the least significant bits of the accumulative data during quantization. By removing these lower-order bits that contribute minimally to the overall accuracy, the system reduces memory requirements while preserving the essential information needed for stress compensation. This extraction approach directly addresses the storage capacity constraint.
2Quantity of substance
If quantization is applied to compress data, then memory capacity is utilized efficiently, but measurement precision deteriorates
Solution Approach 1:
The patent applies dithering as a preliminary action before quantization. By adding random noise to the data prior to reducing precision, the quantization errors are randomized and distributed across multiple measurements. This preliminary dithering action prevents systematic bias in the quantized data, thereby maintaining measurement accuracy even after precision reduction. The dithering ensures that the quantization process does not systematically underestimate or overestimate stress values.
Solution Approach 2:
The patent introduces dithering noise as an intermediary element between the original high-precision data and the quantized low-precision representation. This intermediary random noise facilitates the transition by masking quantization errors and preventing correlation between consecutive quantization mistakes. The dithering mediator preserves the statistical properties of the original data while enabling efficient compression.
3Measurement precision
If dithering is applied to maintain accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements a self-service dithering approach where the system uses its own existing resources (available memory, existing data values) to generate the dithering noise. Rather than requiring external random number generators or complex hardware, the system recycles available data and memory resources to create the necessary randomization. This self-service approach maintains measurement precision while avoiding additional device complexity.
Data Source
AI summary
A method of accumulating data by a processor in a nonvolatile memory to track use of a device. The method includes: retrieving by the processor a next datum for accumulation into a first accumulation stored in the memory, the next datum representing a next use of the device; generating by the processor a next dither offset; adding by the processor the next dither offset to the next datum to produce a first sum; dividing by the processor the first sum by a scale factor to produce a quantized datum; and adding by the processor the quantized datum to the first accumulation. The first accumulation tracks the use of the device.