ROI coding with integer wavelet transforms and unbalanced spatial orientation trees
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
In this paper, we present region-based coding of medical images using integer wavelet transforms and the modified SPIHT algorithm. Our method differs from previously reported region-of- interest (ROI) coders in such a way that the region-based integer wavelet transform is used to obtain the representation of the partitioned image plane rather than differentiating the coefficients associated with each region after using the conventional wavelet decomposition. In fact, this region-based representation retains the properties of the conventional wavelet transform, and thereby facilitates the use of conventional wavelet coefficient plane coders for region-based coding. We propose a novel region-based coder based on the SPIHT algorithm. Previous region-based SPIHT coders employ conventional one-to-four parent-child binding, which accumulates coefficients from different regions within a spatial orientation tree. Alternatively, we present the unbalanced spatial orientation tree structure, which prevents the aforementioned heterogeneity in the tree, and size of which adapts to the size of the region being encoded. In addition to its superior rate-distortion (R-D) performance, the proposed coder offers region-size insensitive coding of the partitioned wavelet coefficient plane.
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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