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Record W2093370911 · doi:10.1109/icip.2012.6466861

A novel high dynamic range image compression scheme of color filter array data for the digital camera pipeline

2012· article· en· W2093370911 on OpenAlexaff
Dohyoung Lee, Konstantinos N. Plataniotis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Signal Denoising Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer sciencePipeline (software)Computer visionColor filter arrayArtificial intelligenceJPEGPreprocessorEnhanced Data Rates for GSM EvolutionHigh dynamic rangeColor spaceData compressionDynamic rangeFilter (signal processing)Color gelImage (mathematics)

Abstract

fetched live from OpenAlex

This paper introduces a novel color filter array (CFA) image compression scheme capable of handling high dynamic range (HDR) representation. The proposed pipeline consists of a series of preprocessing steps followed by a JPEG XR encoding module. A 8-directional edge-sensing mechanism operator is used to reduce estimation error and preserve edge related information during color space conversion. The utilized YCoCg color space allows for a simplified pipeline implementation. Performance of the proposed solution is validated through objective quality assessment metrics and complexity analysis. Extensive experimentation reported in this paper indicates that the proposed solution is suitable for limited resource environments due to low complexity and high performance.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.888
Threshold uncertainty score0.315

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.050
GPT teacher head0.319
Teacher spread0.269 · 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 designBench or experimental
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

Citations4
Published2012
Admission routes1
Has abstractyes

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