MétaCan
Menu
Back to cohort
Record W2094938072 · doi:10.1109/ipta.2010.5586733

Temporal transcoding of H.264/AVC video to the scalable format

2010· article· en· W2094938072 on OpenAlexaff
Hussain Al-Muscati, Fabrice Labeau

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceTranscodingScalable Video CodingMacroblockMotion compensationMotion vectorQuarter-pixel motionBlock-matching algorithmBitstreamMotion estimationData compressionCodecReal-time computingScalabilityMultiview Video CodingEncoding (memory)PixelVideo compression picture typesCoding (social sciences)Computer visionDecoding methodsComputer hardwareArtificial intelligenceAlgorithmVideo processingVideo trackingComputer networkImage (mathematics)Mathematics

Abstract

fetched live from OpenAlex

In this work, a novel implementation of a video transcoder that converts a video sequence encoded with the H.264/AVC standard to a temporally scalable H.264/SVC stream is achieved with the use of a pixel-domain heterogeneous architecture. The input H.264/AVC stream is fully decoded by the transcoder. Macroblock coding modes are extracted from the input stream and are reused to encode the output stream. A set of new motion vectors is computed from the input stream coded motion vectors, and are mapped to either the hierarchical B-frame or zero-delay referencing structures employed by H.264/SVC. These new motion vectors are further subjected to a 3 pixel refinement. As a result, a significant decrease in computational complexity is achieved, while maintaining a close to optimum compression efficiency.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.242
Teacher spread0.226 · 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 designNot applicable
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

Citations14
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicVideo Coding and Compression TechnologiesFrench-language works237,207