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Record W2134977040 · doi:10.1109/11.868929

On the use of frame-based slice size for the robust transmission of MPEG video over ATM networks

2000· article· en· W2134977040 on OpenAlexaff
Luis Orozco–Barbosa, Taeseop Han

Bibliographic record

VenueIEEE Transactions on Broadcasting · 2000
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceRobustness (evolution)Real-time computingMPEG-2Video compression picture typesFrame (networking)MPEG-4Asynchronous Transfer ModeTransmission (telecommunications)Video processingVideo trackingComputer networkComputer hardwareCoding (social sciences)Telecommunications

Abstract

fetched live from OpenAlex

We study the performance requirements for the transport of MPEG video streams over ATM networks. A control scheme is introduced to limit the propagation of impairments through MPEG-based video sequences as a result of cell losses. The scheme assigns different slice sizes to the video frames based on their type and order of appearance within the group of pictures. The main aim of the proposed scheme is to improve the robustness of the video delivery process at a minimum cost. Our results demonstrate the effectiveness of this scheme in reducing the propagation of impairments due to the loss of information during the transmission of MPEG video streams over ATM networks.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.974
Threshold uncertainty score0.513

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 communication0.0000.000
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 head0.037
GPT teacher head0.229
Teacher spread0.192 · 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 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

Citations12
Published2000
Admission routes1
Has abstractyes

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