MétaCan
Menu
Back to cohort
Record W2383705327

Computation Complexity Based on Image Compression Algorithm

2007· article· en· W2383705327 on OpenAlexaff
Wentao Wang

Bibliographic record

VenueOptics & Optoelectronic Technology · 2007
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsSet partitioning in hierarchical treesAlgorithmCoding (social sciences)ComputationWaveletData compressionImage compressionAlgorithmic efficiencyComputer scienceComputational complexity theoryCompression ratioWavelet transformMathematicsArtificial intelligenceImage processingImage (mathematics)Discrete wavelet transformEngineeringStatistics
DOInot available

Abstract

fetched live from OpenAlex

SPIHT algorithm is an easy and effective embedded zero tree coding algorithm. But it needs many repeated calculation and is more complicated, which decreases the coding efficiency. An improved SPIHT algorithm is proposed according to the disadvantage of the former. Effective coefficient information is improved by increasing the zero tree depth and decreasing the position information coefficient under the same conditon of wavelet transformation. The algorithm changes the original scanning sequence in order for parallel processing. Experiments show that it improves the coding efficiency under the same condition of compression 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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.663
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.012
GPT teacher head0.305
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
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

Citations0
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueOptics & Optoelectronic TechnologySame topicAdvanced Data Compression TechniquesFrench-language works237,207