Compression Effects of JPEG and JPEG2000 on Temporal-Bone Images
Bibliographic record
Abstract
The efficient compression of medical images is important for improved storage and network utilization. The Joint Photographic Experts Group (JPEG) baseline compression algorithm has been widely used in medical image compression. In contrast, JPEG2000 is a relative new compression standard. The purpose of this study is to provide a quantitative comparison of JPEG and JPEG2000 compression effects on temporal-bone images. Three types of images are investigated – x-ray microCT, orthogonal-plane fluorescence images (OPFI) and histology images. The image quality with different compressed ratios is evaluated by Peak Signal-to-Noise Ratio (PSNR). The study shows that for our grey-scale images, JPEG2000 compression is superior to JPEG for lossy compression with both high compression ratio (1:64) and low compression ratios (1:4 and 1:8). In the middle range of compression ratios (1:16 – 1:32), JPEG and JPEG2000 have the same effects. For our colour histology images, JPEG2000 is superior to the JPEG at all tested compression ratios.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".