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Record W1997858149 · doi:10.1109/isbmsb.2009.5133798

Quality assessment of High Definition TV distribution over IP networks

2009· article· en· W1997858149 on OpenAlexaff
Omneya Issa, Wei Li, Filippo Speranza, Ron Renaud

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Video Quality Assessment
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsIPTVTestbedComputer scienceVideo qualityQuality of experienceMultimediaQuality (philosophy)PerceptionVideo streamingComputer networkQuality of serviceEngineering

Abstract

fetched live from OpenAlex

High Definition (HD) content delivery over IP networks has become a reality in the market of entertainment technologies. This paper presents a professional testbed deployed to emulate a real use case of delivering high definition TV material over an IPTV network. The quality of the delivered H.264/AVC encoded video was evaluated by common objective video quality assessment tools in emulated environment subject to impairments. Objective measurements were further validated by subjective tests. Based on the obtained results, guidelines were drawn for the prediction of user perceived quality in loss-prone environments and on the user perception tolerance for viewing impaired HD broadcast IPTV.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.358
Teacher spread0.305 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2009
Admission routes1
Has abstractyes

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