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Record W1973185243 · doi:10.1889/1.2785211

3.2: High Dynamic Range Projection Systems

2007· article· en· W1973185243 on OpenAlexaff
Gerwin Damberg, Helge Seetzen, Greg Ward, Wolfgang Heidrich, Lorne Whitehead

Bibliographic record

VenueSID Symposium Digest of Technical Papers · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsUniversity of British ColumbiaDolby (Canada)
Fundersnot available
KeywordsLuminanceHigh dynamic rangeComputer scienceDynamic rangeProjection (relational algebra)Image qualityImplementationComputer visionModulation (music)Computer graphics (images)Liquid-crystal displaySpatial light modulatorArtificial intelligenceImage (mathematics)OpticsPhysics

Abstract

fetched live from OpenAlex

Abstract Digital cinema and home theatre applications need to compete with analog film in terms of image quality. The single most important performance specification of a projection system, and the largest gap in the competition between digital and analog projectors, is the relatively low dynamic range of luminance of current digital projectors. In this paper we introduce a novel digital system capable of displaying images with a high enough dynamic range to rival analog film. The projection system described is based on a serial combination of light modulating devices, such as two liquid crystal micro‐display panels within a projection light engine. One of the modulation steps can be of lower spatial resolution and contrast. This increases the optical efficiency of the system and avoids optical artifacts. We describe several hardware implementations of this approach as well as the required image processing. Finally, we present an evaluation of the designs in terms of performance, image quality and cost.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.019
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.005

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.

Opus teacher head0.005
GPT teacher head0.225
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations17
Published2007
Admission routes1
Has abstractyes

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Same venueSID Symposium Digest of Technical PapersSame topicAdvanced Optical Imaging TechnologiesFrench-language works237,207