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Record W2150036968 · doi:10.1109/tbc.2009.2032801

Bit-Rate Efficiency of H.264 Encoders Measured With Subjective Assessment Techniques

2009· article· en· W2150036968 on OpenAlexaff
Filippo Speranza, A. Vincent, Ron Renaud

Bibliographic record

VenueIEEE Transactions on Broadcasting · 2009
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsEncoderBit (key)Bit rateComputer scienceVideo qualityBit error rateImage qualityHarmonic Vector Excitation CodingQuality (philosophy)MPEG-4Computer hardwareReal-time computingDecoding methodsComputer visionAlgorithmCoding (social sciences)MathematicsStatisticsComputer networkEngineeringImage (mathematics)

Abstract

fetched live from OpenAlex

In this study we evaluated the bit-rate efficiency of current hardware H.264 encoders as compared to that of established MPEG-2 hardware encoders. To estimate bit-rate efficiency, we measured the subjective video quality of MPEG-2 encoded material processed at three bit rates: 8, 12, and 16 Mbps, and determined the bit-rate at which H.264 encoded material produced similar subjective video quality. The MPEG-2 and H.264 bit rates that resulted in the same perceived video quality were used to estimate bit-rate 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)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.023
GPT teacher head0.269
Teacher spread0.245 · 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 designBench or experimental
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

Citations5
Published2009
Admission routes1
Has abstractyes

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