Determining scalar illuminance from cubic illuminance data. Part 2: Tests in real lighting environments and an approach to improve its accuracy
Bibliographic record
Abstract
Scalar illuminance, which describes the constant illumination from all directions, is an important indicator of the abundance of light for a lit object and the adequacy of illumination perceived. This paper proposes a more reliable method to recover scalar illuminance based on tests in natural complex lighting environments. The performance of Cuttle’s Approach 1, Mangkuto’s Approach 2 and Approach 3, together with Xia et al.’s potential Approach 4, were tested under a total of 610 high dynamic range (HDR) panoramic maps of real scenes. The relationships between predicted scalar illuminance and normalised diffuseness levels were checked. The results indicate that the potential Approach 4 is more robust to the cubic meter’s postures, and the predicted scalar illuminance has a regular relationship with normalised diffuseness levels. Approach 4 was corrected, together with Approach 1, formulating a new method named Approach 5S. Later, the proposed Approach 5S was evaluated under 205 indoor and 2233 outdoor panoramas from the Laval HDR databases, and it was shown to recover more reliable scalar illuminance with an average error within 5% in general. This study has provided a practical solution to more accurate vector illuminance-based metrics in real lighting environments. This algorithm can be further integrated into the development of cubic illumination meter instruments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".