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Record W2122638477 · doi:10.1109/itcc.2004.1286728

A novel approach for bi-directional motion estimation and compensation

2004· article· en· W2122638477 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 institutionsConcordia University
Fundersnot available
KeywordsMotion estimationComputer scienceBrightnessDistortion (music)Frame (networking)Motion compensationComputer visionBenchmark (surveying)Block (permutation group theory)Quarter-pixel motionBlock sizeInter frameBlock-matching algorithmArtificial intelligencePixelMotion (physics)Compensation (psychology)Reference frameResidual frameFrame rateMathematicsVideo processingTelecommunicationsBandwidth (computing)OpticsGeography

Abstract

fetched live from OpenAlex

We present a novel bidirectional motion estimation technique, which determines the motion vectors more accurately in situations when there are variations in the inter-frame characteristics due to the changes in the brightness values of the corresponding pixels from frame to frame. The proposed bidirectional motion estimation technique is applied specifically to the fixed-size block, variable-size block and region-wise motion compensation schemes. The proposed method is applied to a number of benchmark video sequences and the results are compared with those obtained by applying the existing methods. These results show that the proposed method can improve the rate-distortion performance. In particular, in situations when there are large variations in the brightness of the corresponding regions from frame to frame, the proposed method improves significantly the rate-distortion performance.

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 score0.194

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 communication0.0000.000
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 head0.041
GPT teacher head0.258
Teacher spread0.217 · 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