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
Recently, many researchers started to question a long-standing paradox in the engineering practice of digital photography: oversampling followed by compression, and pursue more intelligent sparse sampling techniques. In this research we take a practical approach of uniform down sampling in image space, and the sampling is made adaptive by a spatially varying directional low-pass prefiltering. Since the down-sampled prefiltered image is a low-resolution image of conventional square sample grid, it can be compressed and transmitted without any change to current image coding standards and systems. The decoder first decompresses the low-resolution image and then upsamples it to the original resolution by least-square estimation using a 2D piecewise autoregressive model and the knowledge of directional low-pass filter. The proposed joint adaptive down-sampling and up-sampling technique outperforms JPEG 2000 (the state-of-the-art in lossy image coding) in PSNR measure at low to modest bit rates and achieves superior visual quality at all bit rates. This work shows that oversampling not only increases cost and energy consumption, but it could, even when coupled with a sophisticated rate-distortion optimized compression scheme, cause inferior image quality at certain bit rates.
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 | 0.000 |
| 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