Depth map compression based on platelet coding and quadratic curve fitting
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Bibliographic record
Abstract
In this paper, we propose an efficient depth data compression scheme for depth images. Our proposed scheme is a platelet based coding method using quadtree decomposition, Lagrangian optimization function, and quadratic curve fitting algorithm. In this scheme, quadtree decomposition is used to divide a depth map into blocks with one or two homogeneous regions; Lagrangian optimization is employed to minimize the distortion as well as to keep relative high compression ratio within each sub-block; Four quadratic curve fitting models are designed for the objects' contours in order to keep the edges' position more precisely after compression. The experimental results show that our proposed method can achieve compression ratio comparable to the other known platelet based method. Besides, the objects' contours are kept better compared to the other method.
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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 | 0.000 |
| Open science | 0.001 | 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