Calibrated Colour Mapping Between LCD and CRT Displays: A Case Study
Bibliographic record
Abstract
The primary goal of a colour characterization model is to establish a mapping from digital input values di (i=R,G,B) to tristimulus values such as XYZ. A good characterization model should be fast, use a small amount of data, and allow for backward mapping from tristimulus to di. This paper demonstrates implementations of three different colour characterization models, each tested on seven display devices. The characterization models implemented in this study are a 3D LUT, a linear model, and the masking model introduced by Tamura et al. in 2002. The devices include two CRT Monitors, three LCD Monitors, and two LCD Projectors.Several characteristics of the display devices are presented in relation to data collection and characterization modeling. These include the long phosphor stabilization time on CRT monitors and the shifting chromaticity of mixed colours on LCD displays.The results of this study indicate that a simple linear model is the most effective for all devices used in the study, despite the common belief that it is sometimes inappropriate for LCD monitors. A simple extension to the linear model is presented, and it is demonstrated that this extension improves white prediction without causing significant errors for other colours.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".