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Record W1949733609 · doi:10.1109/icip.2001.959213

QoS based video delivery with foveation

2002· article· en· W1949733609 on OpenAlexaff
Anup Basu, Irene Cheng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSparse and Compressive Sensing Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceBandwidth (computing)Data compressionComputer visionImage compressionTransform codingThe InternetArtificial intelligenceComputer graphics (images)Image processingImage (mathematics)Computer networkDiscrete cosine transformWorld Wide Web

Abstract

fetched live from OpenAlex

Spatially varying sensing (foveation) was first used as a means for image compression in our past research. We extend previous work to address the advantages of foveation in improving the performance of MPEG compression over bandwidth limited channels, such as the Internet. Unlike other approaches to foveating MPEG which used multiresolution representations, we use continuously spatially varying resolution and demonstrate that this approach is indeed advantageous over others. Two parameters, scaling and distortion, are used to allow us to adapt MPEG video to various compression ratios. Experimental results are presented, and can be viewed on the Web, validating our approach.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.164
Teacher spread0.150 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2002
Admission routes1
Has abstractyes

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