Medical Imaging Codebook Compression for Unseen Data Encoding
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Solution Overview
Problem
Current data compression methods, particularly entropy encoding, are inefficient in handling previously unseen data and fail to account for mismatch probabilities, leading to suboptimal compression ratios and increased storage demands as data storage capacity lags behind data growth, especially with the rise of multimedia data and transmission bandwidth constraints.
Innovation Solution
A system and method that uses mismatch probability estimation to improve entropy encoding by incorporating a mismatch codeword into the codebook, allowing for efficient handling of unseen data through a secondary encoding process, thereby enhancing compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional entropy encoding methods are used, then compression is achieved for known data patterns, but previously unseen data cannot be efficiently encoded leading to suboptimal compression ratios
Solution Approach 1:
The patent applies preliminary action by pre-training the codebook on representative medical imaging data before actual compression. This training phase prepares the codebook to handle common patterns, while the mismatch probability mechanism is pre-configured to handle unseen data. When encoding, if a mismatch is detected (probability exceeds threshold), the system switches to alternative encoding methods, thus resolving the contradiction between optimizing for known patterns while maintaining capability for unseen data.
2Quantity of substance
If data compression is applied to increase storage capacity, then storage demand is temporarily met, but storage capacity still lags behind exponential data growth especially for multimedia data
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the mismatch probability threshold based on data characteristics and compression requirements. For medical imaging data where precision is critical, the threshold can be set lower to minimize switching to lossy methods. The codebook structure and encoding parameters are optimized specifically for medical imaging patterns, allowing high compression ratios while maintaining diagnostic quality through adaptive parameter selection.
3Loss of energy
If compression ratios are increased to reduce storage demands, then bandwidth requirements decrease, but transmission bandwidth remains a bottleneck for large data sets
Solution Approach 1:
The patent implements feedback mechanisms where the encoder monitors compression performance and data characteristics in real-time. The mismatch probability calculation provides continuous feedback about how well the codebook matches the input data. This feedback loop allows dynamic adjustment of encoding strategies, switching between lossless and lossy methods based on actual performance, thus optimizing bandwidth usage while maintaining data integrity for critical medical imaging information.
Data Source
AI summary
Compression of medical imaging data using codebooks and entropy encoding. Medical imaging data such as tomosynthesis imagery data may be compressed using codewords based on frequency analysis. In an implementation sequential registration technique may be applied to the medical imaging data to create a plurality transformation matrices. The plurality of transformation matrices may be compressed using a matrix codebook. The compressed medical imaging data may be represented as an image codebook and the matrix codebook, providing secure storage and lossless compression of sensitive medical information.