Bibliographic record
Abstract
The transition from traditional 24-bit RGB to high dynamic range (HDR) images is hindered by excessively large file formats with no backwards compatibility. In this paper, we propose a simple approach to HDR encoding that parallels the evolution of color television from its grayscale beginnings. A tone-mapped version of each HDR original is accompanied by restorative information carried in a subband of a standard 24-bit RGB format. This subband contains a compressed ratio image, which when multiplied by the tone-mapped foreground, recovers the HDR original. The tone-mapped image data may be compressed, permitting the composite to be delivered in a standard JPEG wrapper. To naive software, the image looks like any other, and displays as a tone-mapped version of the original. To HDR-enabled software, the foreground image is merely a tone-mapping suggestion, as the original pixel data are available by decoding the information in the subband. We present specifics of the method and the results of encoding a series of synthetic and natural HDR images, using various published global and local tone-mapping operators to generate the foreground images. Errors are visible in only a very small percentage of the pixels after decoding, and the technique requires only a modest amount of additional space for the subband data, independent of image size.
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.
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 | 0.000 | 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) | 0.004 | 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".