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Record W2088783711 · doi:10.1049/el:20030318

Error-free computation of Daubechies wavelets for image compression applications

2003· article· en· W2088783711 on OpenAlex
Khan A. Wahid, Vassil S. Dimitrov, G.A. Jullien, Wael Badawy

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

VenueElectronics Letters · 2003
Typearticle
Languageen
FieldComputer Science
TopicImage and Signal Denoising Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWaveletTransformation (genetics)ComputationImage compressionAlgorithmImage (mathematics)Encoding (memory)Compression (physics)Matrix (chemical analysis)Data compressionDaubechies waveletTransformation matrixAlgebraic numberMathematicsComputer scienceWavelet transformImage processingArtificial intelligenceDiscrete wavelet transformMathematical analysis

Abstract

fetched live from OpenAlex

A novel encoding scheme for Daubechies wavelets is proposed. The technique eliminates the requirements to approximate the transformation matrix elements; rather, by using algebraic integers, it is possible to obtain exact representations for them. As a result, error-free calculations up to the final reconstruction step can be achieved, which provides considerable improvement in image reconstruction accuracy.

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: Methods
Teacher disagreement score0.188
Threshold uncertainty score0.476

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 communication0.0000.000
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 head0.015
GPT teacher head0.287
Teacher spread0.272 · 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