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Record W2038364458 · doi:10.1109/sips.2006.352568

Motion Compensated Frame Rate Conversion Using a Specialized Instruction Set Processor

2006· article· en· W2038364458 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

VenueSiPS ... design and implementation - IEEE Workshop on Signal Processing Systems · 2006
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsComputer scienceAccelerationFrame rateApplication-specific instruction-set processorNTSCSet (abstract data type)Instruction setFrame (networking)Motion estimationBandwidth (computing)Block (permutation group theory)AlgorithmArtificial intelligenceReal-time computingParallel computingHigh-definition televisionMathematics

Abstract

fetched live from OpenAlex

This paper describes an application-specific instruction set for a configurable processor to accelerate motion compensated frame rate conversion (MC-FRC) algorithms based on block motion estimation (BME). The proposed instruction set is generic enough to support many MC-FRC algorithms. The performance gain obtained from this instruction set is described and explained. The new instructions are used to implement two BME algorithms: the full search (FS) algorithm and the one-dimensional full search (ODFS) algorithm. The obtained acceleration factor is about one hundred in the case of the FS algorithm and about forty in the case of ODFS. This paper describes the new instruction set and explains these results by describing more precisely how the acceleration is performed. This paper also shows that the acceleration reached can lead to real-time performance on video streams that have a bandwidth similar to NTSC using processors currently available

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.831
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.070
GPT teacher head0.320
Teacher spread0.250 · 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