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Record W2116022029 · doi:10.1109/wescan.1997.627119

Image compression through fractal surface interpolation and wavelet compression

2002· article· en· W2116022029 on OpenAlexaff
Richard M. Dansereau, Witold Kinsner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Signal Denoising Methods
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFractal transformFractal compressionFractalMathematicsWaveletFractional Brownian motionAffine transformationInterpolation (computer graphics)Lossless compressionNearest-neighbor interpolationComputer visionData compressionArtificial intelligenceImage compressionAlgorithmMathematical analysisLinear interpolationGeometryComputer scienceImage processingImage (mathematics)Brownian motion

Abstract

fetched live from OpenAlex

The paper presents a perceptual image representation technique based on fractal surface interpolation (FSI), This technique is motivated from the observation that images taken from the real world contain many textures that are self similar, or fractal, in nature. The fractal surface interpolation representation is then compressed using a zero tree wavelet compression subsystem with lossless entropy encoding. The fractal surface interpolation technique described relies on the extraction and reconstruction of self affine fractal surfaces with measured Hurst exponents H*. This gives statistically self similar fractal surfaces used to represent textures in a real world image. Fractional Brownian motion (fBm) through a modified midpoint displacement (MPD) algorithm provides the basis for generating these self affine fractal surfaces between interpolation points.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.956
Threshold uncertainty score0.439

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 science0.0000.000
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 head0.033
GPT teacher head0.291
Teacher spread0.258 · 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 designSimulation or modeling
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

Citations2
Published2002
Admission routes1
Has abstractyes

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