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Record W2381254933

Motion Searching Error Concealment for I frame

2008· article· en· W2381254933 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMicrocomputer applications · 2008
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceCoding (social sciences)Error concealmentBlock-matching algorithmVideo qualityComputer visionMotion estimationScalable Video CodingFrame (networking)Reference frameMotion compensationResidual frameVideo processingReal-time computingArtificial intelligenceVideo trackingAlgorithmTelecommunicationsDecoding methods
DOInot available

Abstract

fetched live from OpenAlex

Error resilience is an essential part for video communications, such as digital TV broadcasting,mobile video terminals and video telephone. The latest video compresion standard H.264/AVC provides more coding efficiency for a wide range of video consu-mer applications.Yet H.264 video streams are still vulnerable to transmission errors. A set of error concealment techniques are proposed to provide error resi -lience based on new coding and network characteristics of H.264.The error concealment of I frame espe-cially important for the error of I frame will propagate to the following P or B frame by motion estimation. Comb -ining the temporal and spatial concealment algorithms, an adaptive spatial/ temporal estimation method with low complexity for I frame is presented. Simulations show that the proposed scheme outperforms the existing method both on PSNR and visual quality obviously.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.829
Threshold uncertainty score0.457

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 studies0.0010.000
Scholarly communication0.0000.000
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 head0.048
GPT teacher head0.293
Teacher spread0.245 · 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