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Record W2119964815 · doi:10.1109/icip.2005.1530051

Spatiotemporal-chromatic structure of natural scenes

2005· article· en· W2119964815 on OpenAlexaff
S. Bergner, Mark S. Drew

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBlind Source Separation Techniques
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsChromatic scaleComputer scienceBasis (linear algebra)Encoding (memory)Artificial intelligenceData compressionComputer visionPattern recognition (psychology)Human visual system modelPerceptionPath (computing)Image (mathematics)Mathematics

Abstract

fetched live from OpenAlex

We investigate the implications of a unified spatiotemporal-chromatic basis for compression and reconstruction of image sequences. Different adaptive methods (PCA and ICA) are applied to generate basis functions. While typically such bases with spatial and temporal extent are investigated in terms of their correspondence to human visual perception, here we are interested in their applicability to multimedia encoding. The performance of the extracted spatiotemporal-chromatic patch bases is evaluated in terms of quality of reconstruction with respect to their potential for data compression. The results discussed here are intended to provide another path towards perceptually-based encoding of visual data by examining the interplay of chromatic features with spatiotemporal ones in data reduction.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.780
Threshold uncertainty score0.209

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 science0.0000.000
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 head0.007
GPT teacher head0.248
Teacher spread0.241 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations3
Published2005
Admission routes1
Has abstractyes

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