A Spectral Gamut-Mapping Environment with Rendering Parameter Feedback
Bibliographic record
Abstract
This paper proposes a prototypical environment for gamut mapping in spectral space. Images are rendered in terms of light and material parameters by a symbolic ray tracer, and the parameter ranges are adjusted, without re-rendering, to bring the image into the output device's spectral gamut. There is a growing disparity between the high dynamic range images produced by spectral renderers and the limitations of display gamuts and lowdimensional colour management standards. While in rendering tone mapping has helped compress luminance ranges, and in colour science 3D gamut mapping has helped compress chrominance ranges, only high-dimensional spectral methods will fully bridge the gap. This paper's environment for gamuts in spectral space is a step toward spectral gamut mapping, which we demonstrate by using the ray tracer to predict feasible ranges of rendering parameters for an in-gamut image. The environment can be easily extended to support interactive or automatic image correction, and more sophisticated rendering and gamut-mapping methods of arbitrary dimensionality.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".