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Record W2067765564 · doi:10.1145/1631272.1631492

A compressed-domain spatio-temporal adaptation system for video delivery

2009· article· en· W2067765564 on OpenAlex
Razib Iqbal, Sharmeen Shahabuddin, Shervin Shirmohammadi

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 scienceAdaptation (eye)MetadataBroadcasting (networking)ENCODEDomain (mathematical analysis)Real-time computingDomain adaptationContent adaptationArtificial intelligenceHuman–computer interactionUbiquitous computingComputer networkWorld Wide Web

Abstract

fetched live from OpenAlex

In this demo, we present a working system that we have built for metadata-based compressed-domain spatio-temporal video adaptation of H.264/AVC videos. Our approach is in contrast to most existing approaches that do adaptation in a series of cascaded decode/re-encode procedures. We show that by applying compressed-domain operations, adaptation can be expedited for real-time application scenarios like news/sports broadcasting. Moreover, the system can be applied as a tool box for distributed adaptation within P2P video distribution applications to support heterogeneous devices.

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 score0.899
Threshold uncertainty score0.451

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 head0.028
GPT teacher head0.242
Teacher spread0.214 · 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