Computational projection display : towards efficient high brightness projection in cinema
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cinema projectors need to compete with home theater displays in terms of image quality. High frame rate and high spatial resolution as well as stereoscopic 3D are common features today, but even the most advanced cinema projectors lack in-scene contrast and more importantly high peak luminance, both of which are essential perceptual attributes for images to look realistic. At the same time studies on HDR image statistics suggest that the average image intensity in a controlled ambient viewing environment such as cinema can be as low as 1% for cinematic HDR content and does not often exceed 18%, middle gray in photography. Traditional projection systems form images and colours by blocking the source light from a lamp, therefore attenuating on average between 99% and 82% of light before it reaches the screen. This inefficient use of light poses significant challenges for achieving higher peak brightness levels. We propose a new projector architecture built around commercially available components, in which light can be steered to form images. The gain in system efficiency significantly reduces the total cost of ownership of a projector (fewer components and lower operating cost) and at the same time increases peak luminance and improves black level beyond what is practically achievable with incumbent projector technologies. At the heart of this computational display technology is a new projector hardware design using phase-modulation in combination with new optimization algorithms for real-time phase retrieval. Based on this concept we propose and design a full featured projector prototype. To allow for display of legacy SDR as well as high brightness HDR content on light steering projectors we derive perceptually motivated, calibrated tone mapping and colour appearance models. We develop a calibrated optical forward model of the projector hardware and analyse the impact of content mapping parameters and algorithm choices on (light) power requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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