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
The choice of quantization method in wavelet image compression is a crucial issue that affects performance, quality, and system design. Space-frequency scalar quantization of zero-trees achieves excellent coding efficiency. Stack-run coding is an efficient alternative to zero-trees which maintains independence between subbands. We present a new approach to wavelet quantization which enhances the stack-run coding method. Low addressing complexity, independence between subbands, and fast, parallel decoding are preserved while superior performance is obtained. The most important features are the optimization of dead-zone scalar quantizers, raster scan pattern selection, and the local (spatial) optimization of quantization coefficients. The local optimization is not spatially restricted (as with zero-trees) and new non-recursive optimal algorithms are now possible. Simulation results indicate that the new technique is strongly PSNR competitive with the best of current lossy wavelet image coders. The new framework also allows insight into the nature of the performance gains achieved by space-frequency quantization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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