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Record W2137509276 · doi:10.1109/icassp.2005.1415417

Improvement of JPEG2000 Using Curved Wavelet Transform

2006· article· en· W2137509276 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 institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsWavelet transformSecond-generation wavelet transformWaveletStationary wavelet transformWavelet packet decompositionLifting schemeDiscrete wavelet transformHarmonic wavelet transformArtificial intelligenceJPEG 2000MathematicsComputer visionComputer sciencePattern recognition (psychology)AlgorithmImage compressionImage processingImage (mathematics)

Abstract

fetched live from OpenAlex

The wavelet transform in JPEG2000 is performed using one-dimensional (1D) filtering in the vertical and horizontal directions. This conventional wavelet transform is not effective to represent edges and lines in images. In this paper we present a curved wavelet transform that improves the performance of JPEG200. The curved wavelet transform is performed using 1D filtering along curves that are usually parallel to edges and lines in images. The pixels along these curves can be well represented by a small number of wavelet coefficients. A simple algorithm is proposed in this paper to determine the curves according to image content. Experimental results show that the curved wavelet transform can significantly improve the compression efficiency of JPEG2000, especially for images that contain sharp edges and lines. The coding gain can be up to 1.6 dB in the terms of 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: Methods
Teacher disagreement score0.389
Threshold uncertainty score0.321

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 head0.014
GPT teacher head0.262
Teacher spread0.248 · 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

Citations11
Published2006
Admission routes1
Has abstractyes

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