Bibliographic record
Abstract
The dynamic range in many real-world environments surpasses the capabilities of traditional display technologies by several orders of magnitude. Recently, a novel display capable of displaying images with a dynamic range much closer to real world situations has been demonstrated. This was achieved through a spatially modulated backlight behind an LCD panel. Combined with the modulating power of the LCD panel itself, this enabled the display of much higher contrast compared to an LCD panel with a spatially uniform backlight. In this paper, we describe a further development of the technology, namely a high dynamic range projection system. This makes such display systems more widely applicable as any surface can be used for the display of high dynamic range images. Our new system is designed as an external attachment to a regular DLPTM-based projector, which allows the use of unmodified projectors. It works by adapting the projected image via a set of lenses to form a small image. This small image is then modulated via an LCD panel and the result is projected via another lens system onto a larger screen, as in traditional projection scenarios. The double modulation, by the projector and the LCD panel together, creates a high dynamic range image and an ANSI contrast of over 700:1. Finally, we discuss the advantages and disadvantages of our design relative to other high and low dynamic range display technologies and its potential applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".