Metameric failure assessment and reduction between RGB and laser phosphor projectors
Bibliographic record
Abstract
Abstract Metameric failure is commonly observed when different types of displays reproduce the same color, as it is defined by a colorimetry system, but the outputs do not match visually. Metameric failure is impacted by the used colorimetry system and the relation between the involved displays' spectral power distributions (SPDs). In this work, we assess the metameric failure between the upcoming types of theatrical projectors, RGB laser, and laser phosphor (LaPH) and propose a method to reduce it. Our analysis starts by evaluating the performance of existing colorimetry systems in terms of metameric failure reduction. Among the colorimetry systems tested, the CIE 2006 2° (CIE06 2°) system resulted in the least observed metameric failure for a large portion of the participants but not their absolute majority (>50%). The limited performance of existing colorimetry systems led us to questioning the feasibility of successful perceptual color matching between the two projectors. To explore and potentially rule‐out this scenario, we performed a subjective color matching experiment. The analysis of the results revealed the key role that the projectors' SPD differences play on color matching. Based on the observations of the first two studies, we propose a novel colorimetry system that reduces further than existing colorimetry the systems the metameric failure between RGB and LaPH projectors. Our proposed system is a modified version of CIE06–2° that accounts for the spectral differences of the two light sources. Evaluation showed that our solution outperforms existing colorimetry systems.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".