3D scalable lossless compression of medical images based on global and local symmetries
Bibliographic record
Abstract
We recently proposed a symmetry-based scalable lossless compression method for 3D medical images using the 2D integer wavelet transform and the embedded block coder with optimized truncation (EBCOT). In this paper, we present two major contributions that enhance our early work: 1) a new block-based intra-band prediction method that exploits the global and local symmetries of the wavelet-transform sub-bands based on the main axis of symmetry as detected using the analytical Fourier-Mellin transform; and 2) a new inter-slice DPCM prediction method that exploits the correlation between slices. Performance evaluations on real 3D medical images show an average improvement of up to 17% in lossless compression ratios when compared to the state-of-the-art compression methods including 3D-JPEG2000, JPEG2000 and H.264 intra-coding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".