Data Compression Apparatus for Medical Imaging
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Solution Overview
Problem
Current lossy data compression techniques in medical imaging fail to account for the distortion caused in reconstructed images, leading to uncertain compression ratios and reduced efficiency, as the relationship between raw and reconstructed data is not considered.
Innovation Solution
A data compression apparatus that includes processing circuitry to acquire, compress, decompress, and reconstruct data, determining an optimal compression ratio by comparing reconstructed and decompressed data, using methods like lossy compression with orthogonal transformation and quantization, and employing trained models for improved efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lossy compression processing is applied to raw data to improve compression efficiency, then data compression ratio is improved, but distortion in reconstructed image increases and image quality deteriorates
Solution Approach 1:
The system performs preliminary reconstruction processing on the raw data before compression to generate a reference reconstructed image. This preliminary action allows the system to evaluate the relationship between raw data and reconstructed images in advance, enabling informed compression ratio selection that balances compression efficiency with image quality preservation.
Solution Approach 2:
The system uses feedback from comparing the reconstructed image with the decompressed reconstructed image to determine the appropriate compression ratio. By evaluating the distortion introduced by compression and using this feedback to adjust compression parameters, the system achieves optimal balance between compression ratio and image quality.
2Manufacturing precision
If compression ratio of raw data is lowered to maintain image quality, then image quality is preserved, but data compression efficiency decreases
Solution Approach 1:
The system dynamically adjusts compression parameters by determining an optimal compression ratio based on the specific characteristics of the raw data and the relationship between raw and reconstructed data. This parameter optimization allows the system to achieve high compression efficiency while maintaining acceptable image quality, rather than using a fixed low compression ratio.
3Speed
If lossy compression is applied without considering reconstructed image distortion, then compression speed is improved, but compression ratio becomes uncertain and suboptimal
Solution Approach 1:
The system performs preliminary reconstruction and evaluation before final compression to understand the data characteristics and expected distortion. This preliminary analysis enables the system to select optimal compression ratios that maximize compression efficiency while ensuring image quality requirements are met, rather than using trial-and-error approaches.
Data Source
AI summary
According to one embodiment, a data compression apparatus includes processing circuitry. The processing circuitry generates reconstructed data by performing reconstruction processing on data. The processing circuitry generates decompressed reconstructed data by performing the reconstruction processing on decompressed data obtained by decompressing compressed data that is generated by performing compression processing on the data. The processing circuitry determines a parameter relating to a compression ratio of the data based on comparison between the reconstructed data and the decompressed reconstructed data.