MétaCan
Menu
Back to cohort
Record W2030826568 · doi:10.1889/1.1832558

54.2: A High Dynamic Range Display Using Low and High Resolution Modulators

2003· article· en· W2030826568 on OpenAlexaff
Helge Seetzen, Lorne Whitehead, Greg Ward

Bibliographic record

VenueSID Symposium Digest of Technical Papers · 2003
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHigh dynamic rangeMonochromeRefresh rateLiquid-crystal displayBrightnessDisplay resolutionDisplay deviceDynamic rangeLED displayLuminanceCompensation (psychology)Resolution (logic)High resolutionComputer scienceComputer graphics (images)Materials scienceOpticsComputer visionArtificial intelligenceOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Abstract We have developed an emissive high dynamic range (HDR) display that is capable of displaying a luminance range of 10,000cd/m2 to 0.1cd/m2 while maintaining all features found in conventional LCD displays such as resolution, refresh rate and image quality. We achieve that dynamic range by combining two display systems — a high resolution transmissive LCD and a low resolution, monochrome display composed of high brightness light emitting diodes (LED). This paper provides a description of the technology as well as findings from a supporting psychological study that establishes that correction for the low resolution display through compensation in the high resolution display yields an image which does not differ perceptibly from that of a purely high resolution HDR display.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.005
GPT teacher head0.214
Teacher spread0.209 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations148
Published2003
Admission routes1
Has abstractyes

Explore more

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