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Record W2116096293 · doi:10.1109/lsp.2003.821661

Quadtree-Based Multiregion Multiquality Image Coding

2004· article· en· W2116096293 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

VenueIEEE Signal Processing Letters · 2004
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsQuadtreeComputer scienceJPEG 2000JPEGContext-adaptive binary arithmetic codingArtificial intelligenceComputer visionImage compressionCoding (social sciences)Data compressionCoding tree unitImage resolutionPixelFidelityImage processingAlgorithmDecoding methodsImage (mathematics)Mathematics

Abstract

fetched live from OpenAlex

Region-based video coding schemes employed in MPEG-4 are also promising for still image coding applications where images contain a number of objects that can be encoded at different bit rates, such as compression of medical images for archiving and transmission. Motivated by this fact, in this letter we investigate multiregion multiquality (MRMQ) coding with quadtree-based wavelet coders. We present a novel scheme which addresses the region size sensitivity problem in region-based coding. The proposed method outperforms the region-of-interest (ROI) coding unit of JPEG-2000; it is possible to save 0.3 bits per pixel to attain the same ROI/background fidelity without sacrificing the additional features provided by JPEG-2000, such as resolution scalability and error resilience.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.580
Threshold uncertainty score1.000

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 communication0.0000.002
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 head0.028
GPT teacher head0.298
Teacher spread0.270 · 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