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Record W2148538448 · doi:10.1109/icip.1998.727242

Reconstruction of motion vector missing macroblocks in H.263 encoded video transmission over lossy networks

2002· article· en· W2148538448 on OpenAlex
Shahram Shirani, F. Kossentini, Rabab Ward

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 British Columbia
Fundersnot available
KeywordsMacroblockArtificial intelligenceMotion vectorComputer scienceLossy compressionComputer visionDeblocking filterMotion estimationBlock-matching algorithmQuarter-pixel motionAlgorithmPattern recognition (psychology)Image (mathematics)Decoding methodsVideo processingVideo tracking

Abstract

fetched live from OpenAlex

Errors caused by loss of coded data can seriously affect an H.263 decoded image sequence. Several scenarios may occur that include: (1) loss of macroblocks in I or P frames, and (2) loss of motion vectors of macroblocks in P frames. The missing macroblocks in I and P frames can be reasonably reconstructed by exploiting the correlation between adjacent macroblocks. Existing methods which reconstruct the motion vector of a macroblock rely on existing motion vectors of surrounding macroblocks, and the results are not always satisfactory. A novel reconstruction technique for restoration of macroblocks with missing motion vectors is proposed. This method exploits the image continuity inside and across the borders of the macroblocks. Simulation results indicate that the performance of the proposed algorithm is good, both subjectively and objectively.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.450

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 communication0.0000.000
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 head0.018
GPT teacher head0.221
Teacher spread0.203 · 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