MétaCan
Menu
Back to cohort
Record W4251444399 · doi:10.1145/1276377.1276426

Ldr2Hdr

2007· article· en· W4251444399 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACM Transactions on Graphics · 2007
Typearticle
Languageen
FieldComputer Science
TopicImage Enhancement Techniques
Canadian institutionsDolby (Canada)University of British Columbia
Fundersnot available
KeywordsComputer scienceHigh dynamic rangeComputer graphics (images)Robustness (evolution)Video processingFidelityGraphicsDynamic rangeEmphasis (telecommunications)Artificial intelligenceComputer visionTelecommunications

Abstract

fetched live from OpenAlex

New generations of display devices promise to provide significantly improved dynamic range over conventional display technology. In the long run, evolving camera technology and file formats will provide high fidelity content for these display devices. In the near term, however, the vast majority of images and video will only be available in low dynamic range formats. In this paper we describe a method for boosting the dynamic range of legacy video and photographs for viewing on high dynamic range displays. Our emphasis is on real-time processing of video streams, such as web streams or the signal from a DVD player. We place particular emphasis on robustness of the method, and its ability to deal with a wide range of content without user adjusted parameters or visible artifacts. The method can be implemented on both graphics hardware and on signal processors that are directly integrated in the HDR displays.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.278
Teacher spread0.258 · 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