Calibrated color mapping between LCD and CRT displays: A case study
Bibliographic record
Abstract
Abstract The primary goal of a color characterization model is to establish a mapping from digital input values d i ( 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 d i . The characterization models considered here are for the case of an end user who has no direct knowledge of the internal properties of the display device or its device driver. Three characterization models tested on seven different display devices are presented. The characterization models implemented in this study are a 3D look up table (LUT) (Raja Balasubramanian, Reducing the Cost of Lookup Table Based Color Transformations, Proc IS&T/SID 7th Color Imaging Conference 1999;44:321–327 ), a linear model (Fairchild MD, Wyble DR. Colorimetric Characterization of the Apple Studio Display (Flat Panel LCD). Munsell Color Science Laboratory Technical Report, 1998), and the masking model (Tamura N, Tsumura N, Miyake. Masking Model for accurate colorimetric characterization of LCD. Proc IS&T/SID 10th Color Imaging Conference 2002;312–316 ). The devices include two CRT monitors, three LCD monitors, and two LCD projectors. The results of this study indicate that a simple linear model is the most effective and efficient for all devices used in the study. 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 colors. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 438–447, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".