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Record W2154769360 · doi:10.1109/icassp.2004.1326481

Embedded color image coding using context-modeled wavelet difference reduction

2004· article· en· W2154769360 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChrominanceArtificial intelligenceComputer visionWaveletColor spaceComputer scienceColor imageWavelet transformDiscrete wavelet transformLuminanceColor histogramJPEG 2000MathematicsWavelet packet decompositionPattern recognition (psychology)Image compressionImage processingImage (mathematics)

Abstract

fetched live from OpenAlex

A novel embedded wavelet coding algorithm for color image and video compression is proposed. A color image is first represented in YUV color space. The discrete wavelet transform of each of the three color components is then calculated separately. The luminance component is compressed with context-modeled wavelet difference reduction (CM-WDR). The chrominance components are compressed using the contextual information obtained during the coding of the luminance component. Our experimental results show that the proposed algorithm performs better than two latest wavelet-based benchmark schemes, namely CEZW and JPEG2000, in terms of color SNR and component PSNR.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.438
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

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

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 head0.034
GPT teacher head0.305
Teacher spread0.270 · 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

Quick stats

Citations5
Published2004
Admission routes1
Has abstractyes

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