CT Data Compression Over Slip Rings With Selective Error Protection
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Solution Overview
Problem
Current CT imaging technologies face challenges in efficiently compressing and transmitting high volumes of data while maintaining data integrity, as existing compression algorithms are sensitive to corruption and may not function effectively during low compression periods, leading to potential data loss and insufficient bandwidth for high-resolution scans.
Innovation Solution
A method and system architecture for compressing and decompressing data using a compressor and decompressor system with memory buffers, employing Golomb-Rice encoding and error protection, which allows for flexible compression and error correction to maintain data integrity and adapt to varying bandwidth requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lossless compression algorithms are used to prevent data loss, then data integrity is improved, but compression ratio deteriorates and bandwidth efficiency is reduced
Solution Approach 1:
The system dynamically adapts compression strength based on channel conditions. When error correction is applied, the system can use stronger compression algorithms; when errors are detected or channel conditions are poor, it reduces compression strength or switches to lossless modes. This dynamic adjustment resolves the contradiction by optimizing the compression-integrity tradeoff in real-time based on actual transmission conditions.
Solution Approach 2:
The patent changes compression parameters (compression ratio, algorithm strength, buffer sizes) based on error correction status and channel conditions. By adjusting these parameters dynamically, the system achieves high compression ratios when conditions permit while maintaining data integrity when errors are present, thus resolving the contradiction between compression efficiency and data reliability.
2Reliability
If error correction codes are added to protect against data corruption, then data reliability is improved, but data volume increases reducing compression effectiveness
Solution Approach 1:
The system segments the data transmission into multiple packets with distributed error correction. Instead of applying heavy error correction to the entire data set, it divides data into smaller units and applies lightweight error correction codes to each segment or to critical metadata. This segmentation approach maintains reliability while minimizing the overall overhead added by error correction mechanisms.
Solution Approach 2:
The system applies error correction selectively rather than uniformly to all data. It prioritizes error protection for critical data elements and metadata while using lighter or no error correction for less critical portions. This partial application of error correction maintains data reliability for essential information while minimizing the total data volume increase.
3Productivity
If compression algorithms process data based on previously stored data, then compression ratio is improved, but sensitivity to data corruption increases
Solution Approach 1:
The system performs preliminary error detection and correction before compression processing. By detecting and correcting errors in advance, the compressed data is less likely to propagate corruption through the decompression process. This preliminary action protects against the sensitivity issue while maintaining the benefits of compression algorithms that use historical data.
Solution Approach 2:
The patent introduces error correction codes as an intermediary layer between the compression algorithm and the transmitted data. This intermediary protects the compression process from corruption by detecting and correcting errors before they can affect the compression ratio or decompression accuracy. The error correction acts as a buffer that maintains both compression efficiency and data reliability.
4Productivity
If high compression ratios are achieved to reduce bandwidth requirements, then transmission efficiency is improved, but error resilience deteriorates
Solution Approach 1:
The system dynamically adjusts compression strength based on channel error rates and data criticality. When channel conditions are good, it uses high compression ratios; when errors are detected or conditions deteriorate, it reduces compression strength and increases error protection. This dynamic behavior resolves the contradiction by optimizing the balance between transmission efficiency and error resilience in real-time.
Solution Approach 2:
The system periodically monitors transmission quality and adjusts compression/error protection parameters accordingly. By implementing periodic quality checks and parameter adjustments, the system maintains high transmission efficiency during good conditions while ensuring error resilience when problems arise, thus resolving the contradiction between these two opposing requirements.
Data Source
AI summary
A method and system for transferring data from a rotating data source to a stationary target over a slip ring. The system includes a compressor on a rotor, a decompressor on a stationary platform, and respectively corresponding memory buffers. The data is optionally compressed in the event that the slip ring capacity is below the required rate. The compressor a predictor unit, an entropy encoder unit and an error protector unit. The predictor unit may use stored data as reference from a set of frames and locations within them. Encoding of locations may be carried out with a Golomb-Rice encoded integer, and entropy encoding of differences may involve Golomb-Rice encoding with parameter K varying for each group of data units. The error protector protects parameters inserted into data stream with an error protection code while most of the data can remain without protection.