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Record W2157452251 · doi:10.1109/ccece.2004.1345227

RD optimized, adaptive, error-resilient transmission of MPEG-4 coded video

2004· article· en· W2157452251 on OpenAlexaff
Scott Bezan, Shahram Shirani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBitstreamComputer scienceConvolutional codeCoding (social sciences)Channel (broadcasting)Transmission (telecommunications)Error detection and correctionReal-time computingForward error correctionBit error rateCoding tree unitAlgorithmBandwidth (computing)Channel codeDecoding methodsComputer networkTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

For transmission of video data, source encoding is required to maintain bandwidth constraints and channel coding is useful in suppressing errors introduced through transmission. Furthermore, characteristics of practical channels are time-varying. In this paper, an MPEG-4 coded bitstream is channel coded using rate-compatible punctured convolutional (RCPC) codes. The flexibility of the RCPC codes allows the error protection to be optimally adapted to the condition of the time-varying channel using a Lagrangian optimization technique. The scheme of adaptive error control coding (ECC) to the source coded data is compared with a scheme that optimizes the amount of channel coding data to be added at the outset of transmission of the bitstream.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.783
Threshold uncertainty score0.492

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 head0.027
GPT teacher head0.257
Teacher spread0.230 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations0
Published2004
Admission routes1
Has abstractyes

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