MétaCan
Menu
Back to cohort
Record W2099481393 · doi:10.1109/isce.2011.5973795

New method for concealing entirely lost frames in H.264 video transmission over wireless networks

2011· article· en· W2099481393 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsCommunications Research Centre CanadaInnovation, Science and Economic Development Canada
Fundersnot available
KeywordsComputer scienceMotion compensationMotion vectorComputer visionExtrapolationQuarter-pixel motionArtificial intelligenceError concealmentBlock (permutation group theory)Transmission (telecommunications)Compensation (psychology)Block-matching algorithmBlocking (statistics)Motion estimationMotion (physics)WirelessVideo processingDecoding methodsAlgorithmComputer networkImage (mathematics)Video trackingTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

This paper proposes a new error concealment method in which an improved bidirectional motion copy method is developed to conceal entire frames lost in video transmission. Also an overlapped block motion compensation technique is employed to reduce blocking artifacts in the concealed frames. The simulation results show that the proposed method can achieve a PSNR of 2.21dB higher than the conventional motion copy method and of up to 2.56dB higher than conventional motion vector extrapolation methods.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.913
Threshold uncertainty score0.574

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 head0.041
GPT teacher head0.301
Teacher spread0.260 · 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