OLED Burn-in Statistics Compression via Quantization and Predictive Coding
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Solution Overview
Problem
High-resolution, multiple-channel images storing burn-in statistics for OLED displays consume significant storage space, posing a challenge for computing devices as they increase in size and complexity over the display's lifespan, necessitating efficient compression techniques to reduce storage requirements.
Innovation Solution
Pre-processing image data by quantizing sub-pixel values, applying invertible transformations, predictive coding, and encoding differential values to enhance compression ratios when using lossless compressors like LZW-based compressors, thereby reducing storage needs while maintaining accurate burn-in statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution, multiple-channel images are stored to record burn-in statistics for OLED displays, then the accuracy and detail of burn-in data are improved, but the storage space consumption increases significantly
Solution Approach 1:
The patent extracts only the essential burn-in statistics data from the complete high-resolution image data. By identifying and retaining only the critical pixels that actually contribute to burn-in compensation, the system removes redundant data while preserving measurement precision for the relevant parameters.
Solution Approach 2:
The patent segments the burn-in statistics image into multiple channels (e.g., red, green, blue sub-pixel channels) and processes each channel separately. This segmentation allows for selective compression and storage of only the most significant data from each channel, reducing overall storage requirements while maintaining accuracy where needed.
2Measurement precision
If lossless compression is applied to burn-in statistics images, then the accuracy of burn-in data is preserved, but the compression ratio and storage efficiency are reduced
Solution Approach 1:
The patent applies different compression strategies to different regions and channels of the burn-in statistics image based on their local characteristics. Critical regions that require high precision are compressed with lossless or high-fidelity methods, while less critical regions use more aggressive compression, optimizing the balance between accuracy and storage efficiency.
Solution Approach 2:
The patent applies partial compression to the burn-in statistics data, compressing only the portions of the data that can tolerate some loss of precision while maintaining full precision for critical parameters. This selective approach achieves better storage efficiency than complete lossless compression while preserving necessary accuracy.
Data Source
AI summary
Disclosed herein are techniques for pre-processing image data for compression, e.g., image data that represents burn-in statistics for a display device. The techniques can involve receiving the image data, where the image data comprises a plurality of pixels, and each pixel of the plurality of pixels comprises at least two sub-pixel values. Next, each pixel of the plurality of pixels is quantized to produce a plurality of modified pixels. Subsequently, a series of operations are performed against each modified pixel of the plurality of modified pixels, including (1) applying an invertible transformation against the modified pixel, (2) applying a predictive coding against the modified pixel, and (3) applying an encoding of the modified pixel into a buffer as a data stream. The buffer is then compressed (as the modified pixels are serially encoded into the buffer) to produce compressed outputs that are joined together to produce a compressed image.