MétaCan
Menu
Back to cohort
Record W2165387203 · doi:10.1049/cp.2011.1065

A hybrid image compression technique based on DWT and DCT transforms

2011· article· en· W2165387203 on OpenAlex

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsDiscrete cosine transformImage compressionComputer scienceTransform codingComputer visionData compressionArtificial intelligenceCompression (physics)Image (mathematics)Texture compressionImage processingMaterials science

Abstract

fetched live from OpenAlex

In this paper, a hybrid technique using the discrete cosine transform (DCT) and the discrete wavelet transform (DWT) is presented. We show evaluation and comparative results for DCT, DWT and hybrid DWT-DCT compression techniques. Using the Power Signal to Noise Ratio (PSNR) as a measure of quality, we show that DWT with a two-threshold method named “improved-DWT” provides a better quality of image compared to DCT and to DWT with a one-threshold method. Finally, we show that the combination of the two techniques, named improved-DWT-DCT compression technique, showing that it yields a better performance than DCT-based JPEG in terms of PSNR.

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)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.604
Threshold uncertainty score0.576

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 science0.0010.000
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 head0.019
GPT teacher head0.252
Teacher spread0.232 · 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

Citations31
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Data Compression TechniquesFrench-language works237,207