Image Coding on Quincunx Lattice with Adaptive Lifting and Interpolation
Why this work is in the frame
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Bibliographic record
Abstract
Considering that quincunx lattice is a more efficient spatial sampling scheme than square lattice, we investigate a new approach of image coding for quincunx sample arrangement. The key findings are: 1) adaptive directional lifting is particularly suited to decorrelate samples on quincunx lattice, and 2) quincunx samples can be processed by a 2D piecewise autoregressive model to reproduce the image of conventional square pixel grid, while preserving high frequency spatial features well. By incorporating these two techniques into the encoder and decoder respectively, we are able to improve the performance of JPEG 2000 image codec at low to modest bit rates. Since an image can be easily split into quincunx segments, this work has significance for multiple description image/video coding as well
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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