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Record W2153735711 · doi:10.1109/icicic.2006.394

A Novel Shape Coding Scheme For MPEG-4 Visual Standard

2006· article· en· W2153735711 on OpenAlexaff
Lele Zhou, Saif Zahir

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsContext-adaptive binary arithmetic codingComputer scienceCoding (social sciences)Data compressionTransform codingQuadtreeArtificial intelligenceComputer visionMPEG-4Coding tree unitContext-adaptive variable-length codingVariable-length codeAlgorithmPixelMathematicsDecoding methodsDiscrete cosine transform

Abstract

fetched live from OpenAlex

MPEG-4 standard is an object based coding scheme. Shape representation is a new feature in MPEG-4, which specifies objects’ opaque characteristics. In this paper, we propose a novel shape coding scheme for MPEG-4 visual standard. The proposed coding scheme segments a binary alpha plane (BAP) into a number of binary alpha blocks (BAB) of varying sizes based on the opaque or transparent nature of a video object. The segmentation is done through a quadtree structure. The two-dimensional redundancy within a non-homogeneous BAB are exploited by removing the consecutive identical rows and columns of pixels. At the end, zigzag scan and arithmetic coding are adopted to achieve better compression. The proposed scheme bypasses the overhead in computation of an intermediate contour representation and its associated conversions. Experimental results show that the proposed coding scheme has an overall better compression performance than some other advanced coding techniqu

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.027
GPT teacher head0.284
Teacher spread0.257 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations2
Published2006
Admission routes1
Has abstractyes

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