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Record W2137105127 · doi:10.1109/isspit.2006.270908

An Efficient Statistical Intra-mode Prediction Method for H.264

2006· article· en· W2137105127 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 British Columbia
Fundersnot available
KeywordsCodecComputer scienceIntra-frameAlgorithmFrame (networking)Deblocking filterEncoding (memory)Bit rateSimple (philosophy)Reduction (mathematics)Mode (computer interface)Artificial intelligenceDecoding methodsReal-time computingMathematicsTelecommunications

Abstract

fetched live from OpenAlex

H.264 is a new codec that achieves roughly half of the bit-savings over MPEG2 and MPEG4 for a given quality. One of the features of H.264 is that allows intra frames to be predicted from previously encoded macroblocks in the same frame. This prediction is currently based on a simple algorithm that utilizes the encoded modes of the adjacent macroblocks. While this method is fast, it is often inaccurate, especially at high bitrates. In this paper a new adaptive statistical method is proposed that increases prediction accuracy by over 100% in certain situations. This improvement in accuracy results in the reduction of bits required for each intra frame

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: Methods
Teacher disagreement score0.893
Threshold uncertainty score0.274

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 head0.015
GPT teacher head0.318
Teacher spread0.302 · 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