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Record W2098560698 · doi:10.1109/tencon.1996.608459

Multi-resolution motion estimation at low bit-rates

2002· article· en· W2098560698 on OpenAlex
G.R. Rajugopal, Sawsan M. Mahmoud, Roshdy H. M. Hafez

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsMotion estimationComputer scienceQuantization (signal processing)Quarter-pixel motionMotion compensationBit ratePixelCoding tree unitCoding (social sciences)AlgorithmComputer visionImage resolutionArtificial intelligenceResidualMathematicsReal-time computingDecoding methodsStatistics

Abstract

fetched live from OpenAlex

The performance of multi-resolution motion estimation schemes in low bit-rate video coding are presented. Video frames are individually wavelet decomposed and motion activity is detected using variable block size multi-resolution motion estimation (MRME) schemes. The residual frames are coded using zero tree quantization, followed by arithmetic entropy coding. The MRME schemes are shown to exploit the motion correlation among subbands of different scales. We show that the MRME schemes behave differently, than as shown by Zafar et al. (1993), in low bit rate applications employing zero tree quantization. Simulation results are provided for coding of QCIF resolution (176/spl times/144 pixels) video frames at 10 frames/sec, coded at various low bit-rates. Four different MRME schemes are evaluated and performance comparisons are provided for several low output bit-rates.

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)0.000
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 score0.912
Threshold uncertainty score0.684

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)0.0000.001

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 head0.035
GPT teacher head0.289
Teacher spread0.254 · 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

Quick stats

Citations4
Published2002
Admission routes1
Has abstractyes

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