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Record W1917584725 · doi:10.1109/nsip.2005.1502250

Object-based fractal coding of video sequence

2005· article· en· W1917584725 on OpenAlexaff
Shiping Zhu, Kamel Belloulata, B. Amina, Yang Gao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsComputer scienceMotion compensationComputer visionArtificial intelligenceMultiview Video CodingENCODEVideo compression picture typesData compressionBlock-matching algorithmVideo trackingCoding (social sciences)Inter frameVideo processingFrame (networking)Reference frameMathematicsComputer network

Abstract

fetched live from OpenAlex

Summary form only given. In order to realize the efficient and economical transmission/storage of video sequences, and also the object-based functionality of MPEG-4, we propose a new video sequence compression scheme. The CPM/NCIM fractal coding scheme is applied on each object independently by a prior image segmentation map (alpha plane) which is exactly the same as defined in MPEG-4. We encode the first frames of the video sequence as a "set" using circular prediction mapping (CPM) and encode the remaining frames using noncontractive interframe mapping (NCIM). The CPM and NCIM accomplish the motion estimation/compensation which can exploit the high temporal correlations between the adjacent frames of the video sequence. The tested results with a nature video sequence provides promising performances at low bit rate coding, such as applications in video conferencing. We believe it will be a powerful and efficient technique for object-based video sequence 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.517
Threshold uncertainty score0.316

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 science0.0010.000
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 head0.028
GPT teacher head0.307
Teacher spread0.279 · 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 designBench or experimental
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
Published2005
Admission routes1
Has abstractyes

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