ROI coding with integer wavelet transforms and unbalanced spatial orientation trees
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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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".