MétaCan
Menu
Back to cohort
Record W2109358895 · doi:10.1109/mwscas.2007.4488589

Masked nearly-orthogonal wavelet-based image compression and its application to medical imaging

2007· article· en· W2109358895 on OpenAlexafffund
Lakshmi Sugavaneswaran, M. N. S. Swamy, Chunyan Wang

Bibliographic record

VenueConference proceedings · 2007
Typearticle
Languageen
FieldComputer Science
TopicImage and Signal Denoising Methods
Canadian institutionsConcordia University
FundersConcordia University
KeywordsWaveletBiorthogonal systemImage compressionArtificial intelligenceComputer scienceWavelet transformHuman visual system modelBiorthogonal waveletComputer visionTransformation (genetics)Data compressionWavelet packet decompositionSet partitioning in hierarchical treesCompression (physics)Pattern recognition (psychology)Discrete wavelet transformImage (mathematics)Image processingMaterials science

Abstract

fetched live from OpenAlex

A perceptual non-expansive image compression scheme using the nearly-orthogonal wavelets is presented in this paper. The proposed approach differs from the conventional design scheme by incorporating the human visual system characteristics directly in each sub-band of the wavelet decomposed image. The enhancement in the visual quality of the reconstructed image is achieved by using the proposed contrast sensitivity function masking at each decomposition level during wavelet transformation phase in the compression system. The recently explored biorthogonal nearly coiflet wavelet is used to achieve an improvement in the compression performance for low and medium bit rate applications. Extensive simulations are carried out using the proposed approach and the results compared with those of some of the existing techniques.

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)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.769
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.017
GPT teacher head0.295
Teacher spread0.279 · 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
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 routes2
Has abstractyes

Explore more

Same venueConference proceedingsSame topicImage and Signal Denoising MethodsFrench-language works237,207