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Record W4247840301 · doi:10.1109/icip.2014.7025753

Fast intra mode decision for HEVC based on Transparent Composite Model

2014· article· en· W4247840301 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceCoding (social sciences)Algorithmic efficiencyCoding tree unitAlgorithmContext-adaptive binary arithmetic codingDecision modelComputational complexity theoryOutlierMultiview Video CodingSpeedupReal-time computingArtificial intelligenceMachine learningData compressionParallel computingDecoding methodsMathematicsStatistics

Abstract

fetched live from OpenAlex

Compared with H.264/AVC, the newest video standard called High Efficiency Video Coding (HEVC) further improves video coding performance, but at the price of significant increase in its encoding complexity, especially in intra mode decision due to the adoption of complex block partition scheme and more intra prediction modes. To reduce the intra mode decision complexity in HEVC while maintaining its coding efficiency, in this paper, we first formulate the mode decision as a Bayesian decision problem based on the newly proposed Transparent Composite Model (TCM) and then present an outlier based fast intra mode decision method. Experiments show that for the All-Intra Main test configuration of HEVC, our method reduces, on average, the encoding time by 48% with an insignificant loss in coding efficiency (0.7% BD-Rate increase).

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.668
Threshold uncertainty score0.431

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 integrity0.0000.000
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 head0.037
GPT teacher head0.286
Teacher spread0.249 · 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