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Record W2096103142 · doi:10.1109/pacrim.2005.1517274

Half-pixel accurate motion-estimation using a flexible triangle search

2005· article· en· W2096103142 on OpenAlex

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
TopicVideo Coding and Compression Technologies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPixelEncoderComputer scienceMotion estimationArtificial intelligenceComputer visionAlgorithmSearch algorithmSimplexMathematics

Abstract

fetched live from OpenAlex

In this paper, a technique for half-pixel motion estimation using the flexible triangle search (FTS) algorithm is presented. The FTS algorithm is based on the simplex algorithm and has been presented and analyzed in previous work by authors. The approach proposed in this paper for half-pixel motion estimation is based on combining full- pixel and half-pixel searches into a single stage instead of using two separate search stages as commonly used. The resulting half-pixel FTS, HP-FTS, is implemented and analyzed as part of an H.263 encoder. Results indicate that the computational complexity of the proposed HP-FTS is lower compared to a two-stage (full-pixel first and half-pixel second) FTS algorithm. Further, the compression ratios and the quality of the reconstructed sequences using the proposed HP-FTS are similar to those of the two-stage FTS.

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: none
Teacher disagreement score0.891
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.091
GPT teacher head0.334
Teacher spread0.243 · 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