Adaptation and perceived contrast in natural vs wide-color-gamut lighting
Why this work is in the frame
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Bibliographic record
Abstract
The new generation of wide color gamut lighting and displays substantially increases the range of color contrasts observers may be exposed to. For example, a typical wide gamut illuminant can increase the range of reddish-greenish contrasts by roughly 30%. The perceptual consequences of this exposure remain largely unexplored. In a previous study, we examined how observers adapt to the gamut change simulated by a random temporal sequence of uniform chromaticities, chosen to simulate the same Munsell surfaces when viewed under a wide gamut illuminant or equivalent black body spectrum. In the present work we extended this to more naturalistic viewing conditions, in which the set of colors was shown as random spatial variations within images. The images were Mondrians composed of a dense collage of rectangles, with colors drawn from 36 hue angles uniformly spanning the LM vs S chromatic plane and randomly varied in luminance. Observers simultaneously adapted to rapid sequences of the same surface sets under the two illuminants, on the left and right side of a CRT monitor, and then adjusted the relative LM contrast of pairs of test images to match their perceived contrast. Adaptation to the higher LM contrast images reduced the perceived contrast in the Mondrians for a range of test contrasts, including the contrasts of the adaptors. These effects are consistent with the results observed for the sequential adaptation, and further suggest that exposure to the wider gamut introduced by artificial lighting and displays is likely to induce "artificial" states of adaptation that alter the perceived colorfulness of images. Meeting abstract presented at VSS 2018
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it