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Record W2153577662 · doi:10.1109/icip.2009.5413886

A novel approach for computing and pooling Structural SIMilarity index in the discrete wavelet domain

2009· article· en· W2153577662 on OpenAlex
Soroosh Rezazadeh, Stéphane Coulombe

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Video Quality Assessment
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPoolingStructural similaritySimilarity (geometry)WaveletMetric (unit)Pattern recognition (psychology)Artificial intelligenceComputer scienceMathematicsFeature (linguistics)AlgorithmImage (mathematics)

Abstract

fetched live from OpenAlex

The structural similarity (SSIM) index is an objective metric that gives relatively accurate similarity prediction scores with reasonable complexity. In this paper, an excellent trade-off between accuracy and complexity is presented in the form of a wavelet structural similarity index (WSSI), which is more accurate and less complex than the spatial SSIM index. Like the spatial SSIM index, the WSSI has the feature of boundedness. It computes an edge structural similarity map and an approximation structural similarity map to obtain the final similarity score. A contrast map is introduced in the wavelet domain for pooling structural similarity maps. Experimental results show that the low-complexity WSSI gives a correlation coefficient of 0.9548 between objective and subjective scores, and competes with visual information fidelity (VIF) performance.

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)0.001
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: none
Teacher disagreement score0.924
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.326
Teacher spread0.293 · 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

Citations14
Published2009
Admission routes2
Has abstractyes

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