Optimal Linear RGB-to-XYZ Mapping for Color Display Calibration
Bibliographic record
Abstract
Color display calibration, in part, involves mapping input RGB values to corresponding output values in a standardized color space such as CIE XYZ. A linear model for RGB-to-XYZ mapping is based on a 3-by-3 linear transformation matrix T mapping data from (linearized) RGB to XYZ. Such a mapping is often determined by least squares regression on the difference between predicted and measured XYZ values. However, since displays are calibrated for viewing by human observers, it likely would be better to optimize relative to a perceptually uniform color space. Two new methods are proposed which optimize the total error relative to CIELAB or CIEDE2000. The first method uses weighted least squares with weights based on the rate of change of CIELAB coordinates as a function of change in XYZ. The second method uses Nedler-Mead nonlinear optimization to minimize directly in CIELAB or CIEDE200. Experiments based on calibrating 2 CRT monitors, 3 LCD monitors and 2 LCD projectors show significantly better results than the standard least squares calibration.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".