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Record W2741050126 · doi:10.1109/icc.2017.7996741

A color gamut mapping scheme for backward compatible UHD video distribution

2017· article· en· W2741050126 on OpenAlexaff
Maryam Azimi, Timothee-Florian Bronner, Panos Nasiopoulos, Mahsa T. Pourazad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Video Quality Assessment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGamutComputer sciencePipeline (software)Computer visionComputer graphics (images)Artificial intelligenceColor depthHigh definitionColor imageImage processing

Abstract

fetched live from OpenAlex

The new Ultra High Definition (UHD) standard digital imagery can represent much more color information than High Definition (HD) and Standard Definition (SD). Currently most manufactured displays support UHD colors while UHD is being deployed for content production. However not all service providers have updated their pipeline thoroughly. Thus, the enduser that buys a UHD display would not be able to benefit from the wider UHD color range. In this paper, we propose an invertible gamut mapping from UHD colors to HD colors so that UHD displays can reconstruct UHD colors, while HD displays are addressed directly using legacy video delivery pipeline. The proposed color mapping scheme allows the mapped signal to be converted back to the original signal with minimal perceptual error so that the viewers' quality of experience (QoE) is preserved. Our method includes a parameter that adjusts the trade-off between the quality of the HD content and that of the UHD content. Our experiment results provide a guideline on how to strike a balance between color errors in the mapped signal and the retrieved one.

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 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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.777
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.090
GPT teacher head0.358
Teacher spread0.268 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2017
Admission routes1
Has abstractyes

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