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Record W1970549733 · doi:10.1155/2010/509394

Modeling DV/DVCPRO Standards on Reconfigurable Video Coding Framework

2010· article· en· W1970549733 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Electrical and Computer Engineering · 2010
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsUniversity of Windsor
FundersUniversity of Windsor
KeywordsToolboxCoding (social sciences)Computer scienceMPEG-4Multiview Video CodingScalable Video CodingComputer architectureMultimediaEmbedded systemVideo processingComputer hardwareVideo trackingMotion compensationProgramming languageAlgorithm

Abstract

fetched live from OpenAlex

After more than 20 years, several video coding standards and technologies have been delivered. Less consideration is taken on their commonalities and interoperations. Specification and reference code of case by case is time consuming. The MPEG reconfigurable video coding (RVC) framework is a new standard under development by MPEG. It aims to provide a unified high‐level specification of current MPEG video coding technologies. In this framework, the decoder is built as a configuration of video coding tools taken from MPEG toolbox library. Up to now, MPEG‐4 simple profile and China audio video coding standard (AVS) decoders have been successfully modeled with RVC framework. In this paper, we examine another video standard, that is, DV/DVCPRO, and model it with RVC‐CAL. The flexibility and ease of RVC‐CAL is demonstrated as well as the validation of RVC modeling.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score0.520

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 integrity0.0000.001
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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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