Perceptual Colour Difference Uniformity in High Dynamic Range and Wide Colour Gamut
Bibliographic record
Abstract
Perceptual uniformity is a highly desirable property of colour spaces or colour difference measures where equal level in colour value difference should result in equal perceptual difference. Designing colour spaces or colour difference measures of perceptual uniformity is a long standing problem in colour science. This has become increasingly important with the growing popularity of high dynamic range (HDR) and wide colour gamut (WCG) cameras, content and displays. We design an efficient testing framework to evaluate perceptual uniformity by subjective just noticeable difference (JND) measurement at a wide range of luminance levels followed by coefficient of variation (CV) computation. We carry out subjective testing on RGB, xyY, L*a*b*, YCbCr, CIECAM02-UCS and ICtCp colour spaces and ΔE2000 metric in ITU-R BT 2020 colour gamut across a wide range of luminance levels (from 0.01 to 500 nits) using a professional HDR/WCG display in a carefully controlled dark testing environment. Our results suggest that on average, the ICtCp space performs the best in the current test, but is still distant from achieving perceptual uniformity.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".