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Record W2145016936 · doi:10.1109/icip.2005.1529873

A novel object-based fractal stereo video codec

2005· article· en· W2145016936 on OpenAlexafffund
Shiping Zhu, Kamel Belloulata

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsComputer visionMotion compensationArtificial intelligenceComputer scienceQuadtreeCodecInter frameMotion estimationMultiview Video CodingCoding (social sciences)Block-matching algorithmVideo compression picture typesQuarter-pixel motionVideo trackingReference frameMathematicsVideo processingFrame (networking)

Abstract

fetched live from OpenAlex

A novel object-based fractal stereo video coding scheme with quadtree-based disparity or motion compensation is proposed in this paper. Fractal coding is adopted and each object is encoded independently by a prior image segmentation alpha plane, which is defined exactly as in MPEG-4. The first 2 frames of right video are encoded by using the circular prediction mapping (CPM) and the remaining frames are encoded by using the non contractive interframe mapping (NCIM). The CPM and NCIM methods accomplish the motion estimation/compensation of right video. According to the different coding or user requirements, the spatial correlations between left and right frames can be explored by partial or full affine transformation quadtree-based disparity estimation/compensation, or simply applying CPM/NCIM on left video. The testing results for the nature stereo video sequences provide promising performances at low bitrate coding. We believe it will be a powerful and efficient technique for the object-based stereo video sequences coding.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.954
Threshold uncertainty score0.480

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 head0.017
GPT teacher head0.281
Teacher spread0.264 · 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 designOther design
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

Citations3
Published2005
Admission routes2
Has abstractyes

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