MétaCan
Menu
Back to cohort
Record W2132125297 · doi:10.1109/tce.2006.1649641

Single-Sensor Camera Image Compression

2006· article· en· W2132125297 on OpenAlexaff
Konstantinos N. Plataniotis

Bibliographic record

VenueIEEE Transactions on Consumer Electronics · 2006
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer visionColor filter arrayComputer scienceArtificial intelligenceJPEGImage sensorDigital cameraImage compressionData compressionTransform codingPipeline transportDemosaicingImage processingColor imageBayer filterComputer graphics (images)Color gelImage (mathematics)EngineeringDiscrete cosine transform

Abstract

fetched live from OpenAlex

This paper presents digital camera image compression solutions suitable for the use in single-sensor consumer electronic devices equipped with the Bayer color filter array (CFA). The proposed solutions code camera images available either in the CFA format or as the full-color demosaicked data, thus offering different design characteristics, performance and computational efficiency. Extensive experimentation reported in this paper indicates that pipelines which employ a JPEG 2000 coding scheme achieve significant performance improvements compared to similar processing pipelines equipped with a JPEG coder. Other improvements, both objective and subjective, are observed in terms of color appearance, image sharpness and the presence of visual artifacts in the captured images.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.252
Teacher spread0.240 · 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 designSimulation or modeling
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

Citations26
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Consumer ElectronicsSame topicAdvanced Data Compression TechniquesFrench-language works237,207