Comparing lighting quality evaluations of real scenes with those from high dynamic range and conventional images
Bibliographic record
Abstract
Thirty-nine participants viewed six interior scenes in an office/laboratory building and rated them for brightness, uniformity, pleasantness, and glare. The scenes were viewed in three presentation modes: participants saw the real space and images of the spaces on a 17-inch computer monitor in both conventional and high dynamic range (HDR) mode. HDR mode allowed the high range of luminances in the real scene to be accurately reproduced, with maximum luminances more than 10 times higher than those in the conventional images. For those participants who saw the images before the real spaces (the most relevant order for practical applications), the HDR images were rated as significantly more realistic than the conventional images. However, this effect was limited to scenes with relatively large areas of high luminance, which in this study was represented by scenes with windows and daylight. Ratings of the HDR images were significantly related to simple photometric descriptors of the images in the expected manner: Brightness and glare ratings were positively correlated with overall and elevated luminance, and nonuniformity ratings were positively correlated with luminance variability. These results suggest that for evaluations of visual appearance of interior scenes featuring large areas of high luminance, the HDR method may be used as a surrogate for experiencing a real space both for lighting quality research, and in the design process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".