Representing spectral functions by a composite model of smooth and spiky components for efficient full-spectrum photorealism
Bibliographic record
Abstract
We propose a new model called the "composite model" to represent spectral functions. This model is built on the idea of decomposing all spectral functions into smooth and spiky components, each with its own distinct representation. A smooth spectrum can be expressed with coefficients of a set of given basis functions, and the discrete spikes in a spiky spectrum with their locations and heights. For the smooth part, we propose re-sampling functions that are reconstructed from the coefficients in a linear combination to improve efficiency. Spectral multiplication is thus greatly reduced in complexity. This new model shows remarkable advantages in representing spectral functions with aspect to accuracy, compactness, computational efficiency, portability, and flexibility, and it has a great application potential in color science, realistic image synthesis, and color image analysis. Here we apply it to rendering images involving real spiky illuminants as well as objects with light dispersion. The composite model is shown to surpass other models in these applications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".