Comparison of Modern Compression Standards on Medical Images for Telehealth Applications
Bibliographic record
Abstract
Telehealth applications, such as remote diagnosis and examination, have become more and more popular nowadays. However, the generated large number of medical images and their impractical digital size when it comes to telehealth have brought pressure on communication infrastructures. More specifically, the image processing time has increased dramatically due to the size of the digitized images, while requiring substantial transmission bandwidth and storage space. For these reasons, medical image compression has become a hot research topic. While image compression techniques have evolved over several generations, it remains an open question how these standards will perform when it comes to sensitive oversized medical image content. In this paper, the emerging compression standard High Efficiency Video Coding (HEVC) and the next generation standard Versatile Video Coding (VVC) are evaluated on two different medical image datasets, covering the entire range of quality levels. The results of this study show that the VVC standard overperforms HEVC on raw medical image compression both visually and quantitatively, with the performance gap narrowing when dealing with pre-compressed medical images.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".