Pre-charge Phase Data Compression via Block Average Prediction
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Solution Overview
Problem
Existing image sensor technologies face challenges in efficiently compressing pre-charge phase data due to its lack of spatial correlation, which affects the quality and bandwidth of image data, especially in high-speed camera applications.
Innovation Solution
The implementation of a data compression method that computes prediction values, computes residual data, quantizes it, entropy encodes the quantized data, and refines the inverse quantized data using block average prediction and differential pulse-code modulation (DPCM) techniques, supporting block-level random access properties and reducing bandwidth requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If pre-charge phase data is compressed using traditional methods, then bandwidth is reduced, but compression efficiency is poor due to lack of spatial correlation
Solution Approach 1:
The image data is divided into multiple blocks, and prediction is performed block-by-block. This segmentation allows the compression algorithm to process locally correlated data even when global spatial correlation is weak, improving compression efficiency while maintaining bandwidth reduction
Solution Approach 2:
Prediction values are computed before the actual compression process using block average prediction. This preliminary action creates a reference framework that enables more effective residual encoding, thereby improving compression efficiency without sacrificing bandwidth reduction
2Productivity
If block average prediction and DPCM techniques are used, then compression efficiency is improved, but device complexity increases
Solution Approach 1:
The block average prediction uses data within the same block to generate prediction values, making the system self-sufficient and reducing dependency on external reference data. This self-service approach improves compression efficiency while keeping the algorithm relatively simple and manageable
Solution Approach 2:
Prediction values serve as an intermediary between the original pre-charge data and the final compressed output. This intermediary layer enables efficient residual encoding through DPCM while maintaining a clear and manageable algorithmic structure, balancing compression efficiency with device complexity
3Loss of energy
If residual data is quantized and entropy encoded, then bandwidth is reduced further, but measurement precision of residual data decreases
Solution Approach 1:
Quantization is applied selectively to the residual data, which already contains the difference between actual and predicted values. By applying partial action (quantization) only to this residual component rather than the entire data set, the system achieves further bandwidth reduction while minimizing the impact on overall data precision
Solution Approach 2:
The compression process maintains continuity by sequentially applying prediction, residual computation, quantization, and entropy encoding without breaking the data flow. This continuous processing ensures that precision is preserved as much as possible through each transformation stage while achieving cumulative bandwidth reduction
Data Source
AI summary
Implementations generally relate to pre-charge phase data compression. In some implementations, a method includes computing prediction values for image data, where the image data is pre-charge phase data. The method also includes computing residual data based on the prediction values. The method also includes quantizing the residual data. The method also includes entropy encoding the quantized residual data. The method also includes refining an inverse quantized residual data based on one or more of the residual data and a number of left-over bit-budget after entropy encoding.