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Resilient Video Coding via Improved Motion Compensated Prediction

2012· book-chapter· en· W2488302873 on OpenAlexaff
Sunday Nyamweno, Ramdas Satyan, Fabrice Labeau

Bibliographic record

VenueIGI Global eBooks · 2012
Typebook-chapter
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceLossy compressionMotion compensationRobustness (evolution)Computer visionArtificial intelligenceCoding (social sciences)Data compressionWeightingMathematicsStatistics

Abstract

fetched live from OpenAlex

Motion compensated prediction (MCP) is at the heart of modern video compression standard because of its ability to remove temporal redundancies. However, MCP is responsible for temporal error propagation, which can result in severe quality degradation in lossy environments. In this chapter, the authors present two innovative methods of improving MCP to be more resilient to packet losses. In the first method, the motion trajectory is used to develop a novel distortion weighting technique, and the second method exploits the presence of Intra macroblocks in previously coded frames to develop increase robustness.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.022
GPT teacher head0.234
Teacher spread0.212 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2012
Admission routes1
Has abstractyes

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