Joint space-frequency segmentation, entropy coding and the compression of ultrasound images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Joint space-frequency segmentation is a relatively new image compression technique that finds the rate-distortion optimal representation of an image from a large set of possible space-frequency partitions and quantizer combinations. As such, the method is especially effective when the images to code are statistically inhomogeneous, which is certainly the case in the ultrasound modality. Unfortunately, however, the original paper on space-frequency segmentation neglected to use an actual entropy coder, but instead relied upon the zeroth-order entropy to guide the algorithm. In this work, we fill this gap by comparing actual entropy-coding strategies and their effect on both the resulting segmentations as well as the rate-distortion performance. We then apply the resulting "complete" algorithm to representative ultrasound images. The result is an effective technique that performs significantly better than SPIHT using both objective and subjective measures.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it