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Record W1550004788 · doi:10.1109/vetec.1992.245427

Video coding for very high rate mobile data transmission

2003· article· en· W1550004788 on OpenAlexaff
J. Vaisey, E. Yuen, J.K. Cavers

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsVideophoneComputer scienceCodecCoding (social sciences)Channel (broadcasting)VideotelephonyTeleconferenceVideo captureMobile telephonyComputer networkMultimediaVideo processingReal-time computingMobile radioTelecommunicationsComputer hardware

Abstract

fetched live from OpenAlex

Research on high-capacity mobile communications is approaching modems capable of transmitting 64 kb/s in a 30-kHz channel. This rate has made it possible to conceive of new mobile services, the most ambitious of which is perhaps video. Although data rates of 64 kb/s are thought of as very high in the mobile environment, this is a very low rate for video. Many techniques for implementing video at rates between 64 and 128 kb/s have focused on teleconferencing or videophone over a high-quality channel. The results of the investigation into how to adapt the p*64 video coding standard so that it will be suitable for the coding of QCIF formal (176*144) video sequences for mobile transmission are presented. It is concluded that the codec design presented should be robust enough to allow reasonable quality video transmission over a mobile channel operating at 64 kb/s.>

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)0.000
metaresearch head score (Gemma)0.002
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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.043
GPT teacher head0.317
Teacher spread0.274 · 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

Citations2
Published2003
Admission routes1
Has abstractyes

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