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Record W2076720860 · doi:10.1109/cisp.2010.5648305

A novel bit rate reduction method of H.264/AVC intra coding

2010· article· en· W2076720860 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

Venue2010 3rd International Congress on Image and Signal Processing · 2010
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsEncoderComputer scienceAlgorithmCoding (social sciences)Bit rateOverhead (engineering)Reduction (mathematics)Block (permutation group theory)Computational complexity theoryAlgorithmic efficiencyPixelReal-time computingMathematicsArtificial intelligenceStatistics

Abstract

fetched live from OpenAlex

H.264/AVC intra encoder uses nine prediction modes in 4×4 and 8×8 block unit to reduce the spatial redundancies. Too many intra modes not only increase the encoder complexity but also increase the number of overhead bits. In order to reduce the number of overhead bits and computational cost, an intra mode bit rate reduction (BRR) method is proposed in this paper. In the proposed method, the numbers of prediction modes for each 4×4 and 8×8 block are selected adaptively. Based on the similarities of the reference pixels, each block is classified as one of three categories. This paper also estimates the most probable mode (MPM) from the prediction mode direction of neighboring blocks which have different weights according to their positions. Experimental results confirm that the proposed method saves 12.4% bit rate, improves the video quality by 0.37 dB on average, and requires 37.5% less computations than H.264/AVC intra encoder.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.832
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.305
Teacher spread0.280 · 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