High-dynamic range image projection using an auxiliary MEMS mirror array
Bibliographic record
Abstract
We introduce a new concept to improve the contrast and peak brightness of conventional data projectors. Our method provides a non-homogenous light source by dynamically directing fractions of the light from the projector lamp before it reaches the display mechanism. This will supply more light to the areas that need it most, at the expense of the darker parts of the image. In effect, this method will produce a low resolution version of the image onto the image-forming element. To manipulate the light in this manner, we propose using an intermediate array of microelectromechanical system (MEMS) mirrors. By directing the light away from the dark parts earlier in the display chain, the amount of light that needs to be blocked will be reduced, thus decreasing the black level of the final image. Moreover, the ability to dynamically allocate more light to the bright parts of the image will allow for peak brightness higher than the average maximum brightness of display.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".