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Record W2543457082 · doi:10.1109/have.2004.1391878

Motion-oriented coding scheme for compression of concentric mosaic scene representations

2005· article· en· W2543457082 on OpenAlexaff
Kehua Jiang, Éric Dubois

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceComputer visionBitstreamArtificial intelligenceDecoding methodsCoding tree unitCoding (social sciences)Data compressionContext-adaptive binary arithmetic codingRendering (computer graphics)Motion compensationMotion vectorTunstall codingConcentricRandom accessMotion estimationContext-adaptive variable-length codingAlgorithmMathematics

Abstract

fetched live from OpenAlex

A motion-oriented coding scheme for compressing concentric mosaic representations is designed based on modifications to a standardized block-based hybrid video coding scheme. The motion features of the camera capturing the concentric mosaic representations are exploited to enhance the coding efficiency and improve the decoding flexibility. The coded data is organized in a hierarchical structure into a bitstream. Two-level motion vector representation and estimation is used. Simple one-level side information is embedded to facilitate random access. The optimized coding pattern is investigated to enhance the coding efficiency. Only a small portion of reference frames needs to be accessed and decoded to get a target pixel column. This enables fast data retrieval and rapid rendering. The proposed coding scheme is capable of providing high compression efficiency and fast random selective decoding for concentric mosaic rendering.

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 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 score0.914
Threshold uncertainty score0.248

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 communication0.0000.001
Open science0.0000.000
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 head0.331
Teacher spread0.306 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2005
Admission routes1
Has abstractyes

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