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Record W4361828913 · doi:10.1145/3582177.3582188

VVC Coding Unit Partitioning Decision based on Naive Bayes Theory

2023· article· en· W4361828913 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 institutionsCarleton University
Fundersnot available
KeywordsComputer scienceCoding (social sciences)Context-adaptive binary arithmetic codingCoding tree unitReference softwareEncoding (memory)Algorithmic efficiencyAlgorithmBayes' theoremComputational complexity theoryNaive Bayes classifierBinary numberDecoding methodsArtificial intelligenceMathematicsData compressionStatisticsBayesian probabilityArithmetic

Abstract

fetched live from OpenAlex

Versatile Video Coding (VVC) is the latest video coding standard, which uses a hybrid coding model. VVC achieves 50% bitrate saving compared with High Efficiency Video Coding (HEVC) standard. However, the encoding complexity of VVC is higher. In this work, a fast partition decision algorithm is proposed to reduce the encoding complexity of VVC, and the CU splitting or no splitting is modeled as a binary classification problem based on Naive Bayes theory. This method has good performance and balances encoding efficiency and encoding complexity. Experimental results show that, compared with the VVC reference software model, the proposed algorithm can reduce encoding time by 48.00%, while the loss of the BD-rate is only 1.69%.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.961
Threshold uncertainty score0.682

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.041
GPT teacher head0.289
Teacher spread0.247 · 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