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Record W2153106623 · doi:10.1109/itcc.2002.1000427

H.263 video transcoding for spatial resolution downscaling

2005· article· en· W2153106623 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 Ottawa
Fundersnot available
KeywordsComputer scienceTranscodingMacroblockComputer visionVideo post-processingBlock-matching algorithmMotion compensationVideo compression picture typesInter frameReal-time computingVideo qualityMotion estimationTransmission (telecommunications)Frame (networking)Artificial intelligenceFrame rateMotion vectorUncompressed videoVideo trackingVideo browsingVideo processingReference frameDecoding methodsImage (mathematics)Computer networkAlgorithmTelecommunications

Abstract

fetched live from OpenAlex

In order to allow users to use handheld devices accessing video information, such as downloading and playing video files, there is a need to downscale the compressed video into lower spatial resolution and lower transmission bit rate. In this work, transcoding the compressed H.263 video into low spatial-resolution is discussed and realized. To reduce the computation cost, motion vectors from the incoming video stream are resampled and reused. We propose a novel approach to refine motion vectors adaptively according to the motion of every frame or every macroblock in a frame. The proposed approach can improve the video quality and reduce predictive residues of every frame, hence reduce the transmission bit rate. Implementation results suggest that the proposed approach produces better image quality and lower transmission bit rate than a number of previous approaches.

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: Methods · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score0.336

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 science0.0010.000
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 head0.031
GPT teacher head0.267
Teacher spread0.236 · 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

Citations22
Published2005
Admission routes1
Has abstractyes

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